Textile pilling level detection method, device, equipment and readable medium

Abstract

The present disclosure discloses a method, apparatus, device, and readable medium for detecting the pilling level of textiles. A specific implementation of the method includes: obtaining an initial point cloud training sample set; performing point cloud filtering on the initial point cloud training sample set; performing a first training on an initial object recognition network; determining the trained initial object recognition model as a to-be-trained textile pilling level detection model; obtaining a textile point cloud training sample set; performing a second training on the to-be-trained textile pilling level detection model; determining the trained to-be-trained textile pilling level detection model as a to-be-trained textile pilling level detection model; collecting point cloud data of the to-be-tested textiles; performing segmentation processing on the point cloud data of the to-be-tested textiles; inputting point cloud block information into the textile pilling level detection model; and generating textile pilling level detection information. This implementation improves the accuracy of textile pilling level detection information.

Description

Technical Field

[0001] Embodiments of the present disclosure relate to the field of computer technology, and in particular to a method, device, equipment, and readable medium for detecting the pilling level of textiles. Background Art

[0002] Pilling level testing for textiles is of great significance to textile production. Currently, pilling level testing for textiles is typically performed using a two-dimensional image of the textile.

[0003] However, when using the above method to test the pilling level of textiles, the following technical problems often occur:

[0004] First, pilling level testing is performed on textiles using two-dimensional images. Two-dimensional images are significantly affected by environmental and viewing angle variations during imaging. For example, in dim lighting conditions, pilling can become more difficult to discern, resulting in poor accuracy in the pilling level test. Furthermore, changes in viewing angle can cause deformation or surface texture changes in the textile image, which can result in different appearances of pilling at different angles and potentially lead to unstable pilling level test results.

[0005] Continuing with the technical solution to the aforementioned technical problem 1, the following technical problem often arises: Before performing pilling level testing on textiles, an initial object recognition network needs to be trained. The solution for training the initial object recognition network is generally to directly use unprocessed point cloud data containing the corresponding objects to train the initial object recognition network. However, this conventional solution, which directly uses unprocessed point cloud data containing the corresponding objects to train the initial object recognition network, still presents the following problems:

[0006] Second, the initial object recognition network is trained directly using the unprocessed point cloud data of the corresponding identified objects. The point cloud data of the identified objects usually includes a large amount of data point information, which increases the computational complexity and computing resource requirements of the model training, resulting in a waste of computer computing power resources.

[0007] The above information disclosed in this Background section is only for enhancement of understanding of the background of the inventive concept and therefore it may contain information that does not form the prior art that is already known to a person of ordinary skill in the art. Summary of the Invention

[0008] The content of this disclosure is used to briefly introduce concepts that will be described in detail in the detailed description section below. The content of this disclosure is not intended to identify key features or essential features of the claimed technical solution, nor is it intended to limit the scope of the claimed technical solution.

[0009] Some embodiments of the present disclosure provide a method, device, electronic device, and computer-readable medium for detecting the pilling level of textiles to solve one or more of the technical problems mentioned in the background technology section above.

[0010] In a first aspect, some embodiments of the present disclosure provide a method for detecting the pilling level of textiles, the method comprising: obtaining an initial point cloud training sample set; performing point cloud filtering processing on the initial point cloud training sample set to obtain a target point cloud training sample set; performing a first training on an initial object recognition network based on the target point cloud training sample set to obtain a trained initial object recognition model; determining the trained initial object recognition model as a to-be-trained textile pilling level detection model; obtaining a textile point cloud training sample set; performing a second training on the to-be-trained textile pilling level detection model based on the textile point cloud training sample set to obtain a trained to-be-trained textile pilling level detection model; determining the trained to-be-trained textile pilling level detection model as a to-be-trained textile pilling level detection model. The method comprises the steps of: collecting three-dimensional point cloud data of the textile to be detected as the point cloud data of the textile to be detected, wherein the point cloud data of the textile to be detected includes information of each data point, and each data point information in the each data point information includes coordinate position information and color information; segmenting the point cloud data of the textile to be detected to obtain a point cloud block information set, wherein each point cloud block information in the point cloud block information set includes information of each data point; for each point cloud block information in the point cloud block information set, inputting the point cloud block information into the textile pilling level detection model to obtain local pilling level information of the textile corresponding to the point cloud block information; and generating textile pilling level detection information corresponding to the textile to be detected based on the obtained local pilling level information of each textile.

[0011] In a second aspect, some embodiments of the present disclosure provide a device for detecting the pilling level of textiles, the device comprising: a first acquisition unit configured to acquire an initial point cloud training sample set; a point cloud filtering processing unit configured to perform point cloud filtering processing on the initial point cloud training sample set to obtain a target point cloud training sample set; a first training unit configured to perform a first training on an initial object recognition network based on the target point cloud training sample set to obtain a trained initial object recognition model; a first determination unit configured to determine the trained initial object recognition model as a to-be-trained textile pilling level detection model; a second acquisition unit configured to acquire a textile point cloud training sample set; a second training unit configured to perform a second training on the to-be-trained textile pilling level detection model based on the textile point cloud training sample set to obtain a trained textile pilling level detection model; and a second determination unit configured to determine the trained textile to-be-trained textile pilling level detection model. The product pilling level detection model is determined as the textile pilling level detection model; the acquisition unit is configured to acquire three-dimensional point cloud data of the textile to be detected as the textile point cloud data to be detected, wherein the above-mentioned textile point cloud data to be detected includes information of each data point, and each data point information in the above-mentioned data point information includes coordinate position information and color information; the segmentation processing unit is configured to segment the above-mentioned textile point cloud data to be detected to obtain a point cloud block information set, wherein each point cloud block information in the above-mentioned point cloud block information set includes information of each data point; the input unit is configured to input the above-mentioned point cloud block information into the above-mentioned textile pilling level detection model for each point cloud block information in the above-mentioned point cloud block information set to obtain textile local pilling level information corresponding to the above-mentioned point cloud block information; and the generation unit is configured to generate textile pilling level detection information corresponding to the above-mentioned textile to be detected based on the obtained local pilling level information of each textile.

[0012] In a third aspect, some embodiments of the present disclosure provide an electronic device comprising: one or more processors; a storage device on which one or more programs are stored, and when the one or more programs are executed by one or more processors, the one or more processors implement the method described in any implementation of the first aspect above.

[0013] In a fourth aspect, some embodiments of the present disclosure provide a computer-readable medium having a computer program stored thereon, wherein when the program is executed by a processor, the method described in any implementation of the first aspect is implemented.

[0014] The above-described embodiments of the present disclosure have the following beneficial effects: The textile pilling level detection methods of some embodiments of the present disclosure improve the accuracy of textile pilling level detection information and the stability of detection results. Specifically, the poor accuracy and stability of textile pilling level detection results are caused by the fact that when performing pilling level detection on textiles using two-dimensional images of the textile, two-dimensional images are significantly affected by environmental and viewing angle changes during imaging. For example, in dim lighting conditions, pilling may become more difficult to distinguish, resulting in poor accuracy of the textile pilling level detection information. Furthermore, changes in viewing angle may cause deformation or changes in surface texture of the textile in the image, which may cause pilling to appear different at different angles and may lead to unstable pilling level detection results. Based on this, the textile pilling level detection methods of some embodiments of the present disclosure first obtain an initial point cloud training sample set. This obtains the initial point cloud training sample set used to generate a target point cloud training sample set. Then, point cloud filtering is performed on the initial point cloud training sample set to obtain a target point cloud training sample set. This obtains the target point cloud training sample set. Next, the initial object recognition network undergoes a first training phase based on the target point cloud training sample set, obtaining a trained initial object recognition model. This allows the initial object recognition network to be trained using the target point cloud training sample set, improving its processing capability and adaptability to input point cloud data. The target point cloud training sample set during the first training phase helps the model form a smoother decision boundary, thereby reducing the risk of overfitting in the subsequent textile pilling level detection model to be trained. The trained initial object recognition model is then determined as the textile pilling level detection model to be trained. Next, a textile point cloud training sample set is obtained. This yields a textile point cloud training sample set for training the textile pilling level detection model to be trained. Next, a second training phase is performed on the textile pilling level detection model to be trained based on the textile point cloud training sample set, obtaining a trained textile pilling level detection model to be trained. The trained textile pilling level detection model to be trained is determined as the textile pilling level detection model. This yields a textile pilling level detection model for use in textile testing. Next, three-dimensional point cloud data of the textile to be inspected is collected as the textile point cloud data to be inspected. The textile point cloud data to be inspected includes information about individual data points, each of which includes coordinate position information and color information. This generates the three-dimensional point cloud data of the textile to be inspected. The textile point cloud data to be inspected is then segmented to generate a point cloud block information set, where each point cloud block in the point cloud block information set includes information about individual data points.Thus, a point cloud block information set for generating local pilling level information for each textile can be obtained. Next, for each point cloud block in the point cloud block information set, the point cloud block information is input into the textile pilling level detection model to obtain the local pilling level information for the textile corresponding to the point cloud block information. Thus, the point cloud block information set representing the three-dimensional point cloud data of the textile to be tested can be input into the textile pilling level detection model to perform local pilling detection and obtain local pilling level information for each textile. Finally, based on the obtained local pilling level information for each textile, textile pilling level detection information corresponding to the textile to be tested is generated. This is also because the textile pilling level detection model generates local pilling level information for each textile corresponding to the three-dimensional point cloud data of the textile to be tested. The three-dimensional point cloud data of the textile to be tested can not only capture the planar features of the textile surface, but also reflect its three-dimensional structure and subtle changes on the surface. In addition, the detection based on the three-dimensional point cloud data of the textile to be tested is less affected by changes in lighting and viewing angle, thereby improving the accuracy of the textile pilling level detection information and the stability of the detection results. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] The above and other features, advantages, and aspects of the various embodiments of the present disclosure will become more apparent with reference to the following detailed description in conjunction with the accompanying drawings. Throughout the drawings, the same or similar reference numerals represent the same or similar elements. It should be understood that the drawings are schematic and that components and elements are not necessarily drawn to scale.

[0016] Figure 1 is a flow chart of some embodiments of a method for detecting the pilling level of textiles according to the present disclosure;

[0017] Figure 2 1 is a schematic structural diagram of some embodiments of a device for detecting the pilling level of textiles according to the present disclosure;

[0018] Figure 3 It is a structural diagram of an electronic device suitable for implementing some embodiments of the present disclosure. DETAILED DESCRIPTION

[0019] Embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. Although certain embodiments of the present disclosure are shown in the accompanying drawings, it should be understood that the present disclosure can be implemented in various forms and should not be construed as being limited to the embodiments described herein. On the contrary, these embodiments are provided to provide a more thorough and complete understanding of the present disclosure. It should be understood that the drawings and embodiments of the present disclosure are for illustrative purposes only and are not intended to limit the scope of protection of the present disclosure.

[0020] It should also be noted that, for ease of description, only the parts related to the invention are shown in the drawings. In the absence of conflict, the embodiments and features in the embodiments of the present disclosure may be combined with each other.

[0021] It should be noted that the concepts of "first" and "second" mentioned in this disclosure are only used to distinguish different devices, modules or units, and are not used to limit the order or interdependence of the functions performed by these devices, modules or units.

[0022] It should be noted that the modifications of "one" and "multiple" mentioned in the present disclosure are illustrative rather than restrictive, and those skilled in the art should understand that unless otherwise clearly indicated in the context, they should be understood as "one or more".

[0023] The names of the messages or information exchanged between multiple devices in the embodiments of the present disclosure are only used for illustrative purposes and are not used to limit the scope of these messages or information.

[0024] The present disclosure will be described in detail below with reference to the accompanying drawings and in conjunction with embodiments.

[0025] Figure 1 The process 100 of some embodiments of the method for detecting the pilling level of textiles according to the present disclosure is shown. The method for detecting the pilling level of textiles comprises the following steps:

[0026] Step 101: Obtain an initial point cloud training sample set.

[0027] In some embodiments, an entity executing the method for detecting the pilling level of textiles (e.g., a computing device) can obtain an initial point cloud training sample set via a wired or wireless connection. This initial point cloud training sample set can be a training dataset used to train a neural network. Each initial point cloud training sample in this initial point cloud training sample set can include open-source non-textile 3D point cloud data and an item identifier corresponding to the non-textile 3D point cloud data.

[0028] It should be noted that the above-mentioned wireless connection methods may include but are not limited to 3G / 4G connection, WiFi connection, Bluetooth connection, WiMAX connection, Zigbee connection, UWB (ultra wideband) connection, and other wireless connection methods currently known or to be developed in the future.

[0029] Step 102: Perform point cloud filtering on the initial point cloud training sample set to obtain a target point cloud training sample set.

[0030] In some embodiments, the execution entity may perform point cloud filtering on the initial point cloud training sample set to obtain a target point cloud training sample set.

[0031] In some optional implementations of some embodiments, the execution entity may perform point cloud filtering on the initial point cloud training sample set to obtain a target point cloud training sample set through the following steps:

[0032] In the first step, for each initial point cloud training sample in the above initial point cloud training sample set, perform the following update steps:

[0033] In the first sub-step, the sample initial point cloud data included in the initial point cloud training sample is determined as the point cloud data to be updated.

[0034] In the second sub-step, the coordinate position information included in the information of each data point in the above-mentioned point cloud data to be updated is determined as a coordinate position information set. Each coordinate position information in the above-mentioned coordinate position information includes a horizontal coordinate, a vertical coordinate, and a vertical coordinate. The above-mentioned coordinate position information can represent the spatial position of the data point. It should be noted that the coordinate values in the above-mentioned coordinate position information set are not negative numbers. In practice, the above-mentioned execution entity can determine the horizontal coordinate with the largest value in the above-mentioned coordinate position information set as the first target horizontal coordinate. The horizontal coordinate with the smallest value in the above-mentioned coordinate position information set is determined as the second target horizontal coordinate. The vertical coordinate with the largest value in the above-mentioned coordinate position information set is determined as the first target vertical coordinate. The vertical coordinate with the smallest value in the above-mentioned coordinate position information set is determined as the second target vertical coordinate. The vertical coordinate with the largest value in the above-mentioned coordinate position information set is determined as the first target vertical coordinate. The vertical coordinate with the smallest value in the above-mentioned coordinate position information set is determined as the second target vertical coordinate.

[0035] The third sub-step is to generate the first target horizontal coordinate, the second target horizontal coordinate, the first target vertical coordinate, the second target vertical coordinate, the first target vertical coordinate, and the second target vertical coordinate based on the above-mentioned coordinate position information set.

[0036] The fourth sub-step is to generate the space information to be divided based on the first target horizontal coordinate, the second target horizontal coordinate, the first target vertical coordinate, the second target vertical coordinate, the first target vertical coordinate, and the second target vertical coordinate. In practice, the execution entity may generate the space information to be divided based on the first target horizontal coordinate, the second target horizontal coordinate, the first target vertical coordinate, the second target vertical coordinate, the first target vertical coordinate, and the second target vertical coordinate using a bounding box algorithm. The space information to be divided may be the coordinates of each vertex of a spatial prism. As an example, the first target horizontal coordinate may be represented by xmax. The second target horizontal coordinate may be represented by xmin. The first target vertical coordinate may be represented by ymax. The second target vertical coordinate may be represented by ymin. The first target vertical coordinate may be represented by zmax. The second target vertical coordinate may be represented by zmin. The above-mentioned spatial information to be divided can be "(xmin, ymin, zmin), (xmax, ymin, zmin), (xmin, ymax, zmin), (xmax, ymax, zmin), (xmin, ymin, zmax), (xmax, ymin, zmax), (xmin, ymax, zmax), (xmax, ymax, zmax)".

[0037] In a fifth sub-step, the three-dimensional space corresponding to the to-be-divided spatial information is determined as the bounding space of the spatial points corresponding to the respective data point information. Each of the data point information includes coordinate position information and color information. The coordinate position information may be a three-dimensional spatial coordinate. The color information may be a pixel value representing the corresponding data point.

[0038] The sixth sub-step is to divide the enclosed space into three-dimensional grids. In the three-dimensional grids, the lengths of the corresponding sides of every two three-dimensional grids are equal. In practice, the execution entity may divide the enclosed space into three-dimensional grids using voxelization technology.

[0039] The seventh sub-step is to perform the following filtering steps on each of the three-dimensional grids:

[0040] Sub-step 1: determining each data point information of the spatial point corresponding to the point cloud data to be updated in the above three-dimensional grid as each data point information to be filtered.

[0041] Sub-step 2: In response to determining that the number of the data point information to be filtered in the above-mentioned data point information to be filtered is greater than a preset value, at least one outlier data point information is determined from the above-mentioned data point information to be filtered. In practice, the above-mentioned execution entity can detect each data point corresponding to each data point information to be filtered through the LOF outlier factor detection algorithm to obtain at least one outlier data point information. One outlier data point information in the above-mentioned at least one outlier data point information can represent a data point within a preset spatial range where the data point corresponding to the above-mentioned outlier data point information is located, and the data point density is less than or equal to a preset value. The above-mentioned preset spatial range can represent a spherical space with the data point corresponding to the above-mentioned outlier data point information as the sphere center and a preset radius as the sphere radius.

[0042] Sub-step three: deleting the at least one outlier data point information from the various data point information to be filtered, so as to update the various data point information to be filtered.

[0043] Sub-step 4: determining the updated information of each to-be-filtered data point as sample updated point cloud data.

[0044] Sub-step five: updating the sample initial point cloud data included in the initial point cloud training sample to the sample update point cloud data, so as to update the initial point cloud training sample.

[0045] In the second step, the initial point cloud training sample set after each initial point cloud training sample is updated is determined as the target point cloud training sample set.

[0046] The above technical solution and its related contents, as an inventive point of an embodiment of the present disclosure, solve the second technical problem mentioned in the background technology, which is "directly using the unprocessed point cloud data corresponding to the identified object to train the initial object recognition network. The point cloud data of the identified object usually includes a large amount of data point information, which increases the computational complexity and computational resource requirements of the model training, resulting in a waste of computer computing resources." The factors that lead to the waste of computer computing resources are often as follows: directly using the unprocessed point cloud data corresponding to the identified object to train the initial object recognition network. The point cloud data of the identified object usually includes a large amount of data point information, which increases the computational complexity and computational resource requirements of the model training, resulting in a waste of computer computing resources. If the above factors are solved, the effect of reducing the waste of computer computing resources can be achieved. In order to achieve this effect, first, for each initial point cloud training sample in the above initial point cloud training sample set, the following update steps are performed: In the first sub-step, the sample initial point cloud data included in the above initial point cloud training sample is determined as the point cloud data to be updated. In a second sub-step, the coordinate position information included in each data point in the point cloud data to be updated is determined as a coordinate position information set, where each coordinate position information in the coordinate position information includes a horizontal coordinate, a vertical coordinate, and a vertical coordinate. In a third sub-step, based on the coordinate position information set, a first target horizontal coordinate, a second target horizontal coordinate, a first target vertical coordinate, a second target vertical coordinate, a first target vertical coordinate, and a second target vertical coordinate are generated. Thus, the first target horizontal coordinate, the second target horizontal coordinate, the first target vertical coordinate, the second target vertical coordinate, the first target vertical coordinate, and the second target vertical coordinate are obtained for generating the spatial information to be divided. In a fourth sub-step, the spatial information to be divided is generated based on the first target horizontal coordinate, the second target horizontal coordinate, the first target vertical coordinate, the second target vertical coordinate, the first target vertical coordinate, and the second target vertical coordinate. Thus, spatial information to be divided representing the three-dimensional space to be divided is obtained. In a fifth sub-step, the three-dimensional space corresponding to the spatial information to be divided is determined as the bounding space within which the spatial points corresponding to the data point information reside. Thus, the bounding space used to generate the three-dimensional mesh is obtained. The sixth sub-step is to divide the enclosed space into a three-dimensional grid, wherein the corresponding sides of every two three-dimensional grids in the three-dimensional grid are equal in length. The seventh sub-step is to perform the following filtering steps for each three-dimensional grid in the three-dimensional grid: Sub-step 1 is to determine the individual data point information corresponding to the spatial point of the point cloud data to be updated in the three-dimensional grid as the individual data point information to be filtered. In this way, the individual data point information to be filtered can be obtained. Sub-step 2 is to determine at least one outlier data point information from the individual data point information to be filtered in response to determining that the number of the data point information to be filtered in the individual data point information to be filtered is greater than a preset value.Thus, at least one outlier data point information can be obtained for updating each data point information to be filtered. Sub-step three: Delete the at least one outlier data point information from each data point information to be filtered to update each data point information to be filtered. Thus, at least one outlier data point information can be deleted from each data point information to be filtered, while retaining the original shape features of the point cloud corresponding to each data point information to be filtered, thereby reducing the amount of data. Sub-step four: Determine each updated data point information to be filtered as sample updated point cloud data. Thus, sample updated point cloud data representing the removal of at least one outlier data point information can be obtained. Sub-step five: Update the sample initial point cloud data included in the initial point cloud training sample to the sample updated point cloud data to update the initial point cloud training sample. Thus, the initial point cloud training sample can be updated to the sample updated point cloud data with the outlier data point information removed. Finally, the initial point cloud training sample set after the update of each initial point cloud training sample is determined as the target point cloud training sample set. Thus, a target point cloud training sample set can be obtained after outlier data point information is removed and the data volume is reduced. Furthermore, because the initial point cloud data included in each initial point cloud training sample in the initial point cloud training sample set is divided, the information of each data point to be filtered corresponding to each three-dimensional grid is obtained. Then, the outlier data point information is deleted from the information of each data point to be filtered corresponding to each three-dimensional grid, while retaining the original shape features of the point cloud corresponding to each data point to be filtered, and the data volume is reduced. This reduces the amount of training data in the target point cloud training sample set, reduces the computational complexity and computing resource requirements of model training, and reduces the waste of computer computing resources.

[0047] Step 103 : Perform a first training on the initial object recognition network based on the target point cloud training sample set to obtain a trained initial object recognition model.

[0048] In some embodiments, the execution entity may perform a first training on the initial object recognition network based on the target point cloud training sample set to obtain a trained initial object recognition model.

[0049] In some optional implementations of some embodiments, the execution entity may perform a first training on the initial object recognition network based on the target point cloud training sample set to obtain a trained initial object recognition model through the following steps:

[0050] In the first step, the following first training steps are performed based on the target point cloud training sample set:

[0051] In a first sub-step, the sample point cloud data of at least one target point cloud training sample in the target point cloud training sample set is input into an initial object recognition network to obtain an object identification identifier corresponding to each target point cloud training sample in the at least one target point cloud training sample. The object identification identifier may be an object name.

[0052] In a second sub-step, the sample target item identifier corresponding to each target point cloud training sample in the at least one target point cloud training sample is compared with the object identification identifier corresponding to the target point cloud training sample. In practice, the execution entity may use a cross-entropy loss function to compare the sample target item identifier and the corresponding object identification identifier to determine the difference between the sample target item identifier and the corresponding object identification identifier.

[0053] The third sub-step is to determine whether the initial object recognition network has achieved a preset optimization goal based on the comparison result. The optimization goal may be that the cross entropy loss function value is less than or equal to a preset value.

[0054] In a fourth sub-step, in response to determining that the initial object recognition network has achieved the optimization goal, the initial object recognition network is used as a trained initial object recognition model.

[0055] In a fifth sub-step, in response to determining that the initial object recognition network has not achieved the optimization goal, network parameters of the initial object recognition network are adjusted, and unused target point cloud training samples are used to form a target point cloud training sample set. The adjusted initial object recognition network is used as the initial object recognition network, and the first training step is performed again. As an example, a back propagation algorithm (BP algorithm) can be used to adjust the network parameters of the initial object recognition network.

[0056] Step 104 : determining the trained initial object recognition model as the textile pilling level detection model to be trained.

[0057] In some embodiments, the execution entity may determine the trained initial object recognition model as the textile pilling level detection model to be trained.

[0058] Step 105: Obtain a textile point cloud training sample set.

[0059] In some embodiments, the execution entity may obtain a textile point cloud training sample set. In practice, the execution entity may obtain the textile point cloud training sample set via a wired connection or a wireless connection. The textile point cloud training sample set may be a training data set for performing a second training on the trained initial object recognition model. Each textile point cloud training sample in the textile point cloud training sample set includes sample textile point cloud data and sample target pilling level information corresponding to the sample textile point cloud data. The sample textile point cloud data may be point cloud data of a textile. The sample target pilling level information may indicate the degree of pilling of the textile. For example, the sample target pilling level information may be level "1".

[0060] Step 106 : performing a second training on the to-be-trained textile pilling level detection model based on the textile point cloud training sample set to obtain a trained to-be-trained textile pilling level detection model.

[0061] In some embodiments, the execution entity may perform a second training on the to-be-trained textile pilling level detection model based on the textile point cloud training sample set to obtain a trained to-be-trained textile pilling level detection model.

[0062] In some optional implementations of some embodiments, the execution entity may perform a second training on the to-be-trained textile pilling level detection model based on the textile point cloud training sample set to obtain a trained to-be-trained textile pilling level detection model through the following steps:

[0063] In the first step, the following second training step is performed based on the textile point cloud training sample set:

[0064] In the first sub-step, for each textile point cloud training sample in the textile point cloud training sample set, the following steps are performed:

[0065] Sub-step 1: Inputting the sample textile point cloud data included in the textile point cloud training sample into the point cloud data input layer to obtain each segmented point cloud data. The point cloud data input layer may be an input layer that takes the sample textile point cloud data as input and outputs each segmented point cloud data. Each segmented point cloud data may be a point cloud patch corresponding to each non-overlapping region. Each non-overlapping region may be a spatial region obtained by spatially segmenting the sample textile point cloud data. One of the point cloud patches may be a subset of the sample textile point cloud data.

[0066] Sub-step 2: Input each segmented point cloud data into the point cloud embedding layer to obtain segmented point cloud data vectors corresponding to each segmented point cloud data. The point cloud embedding layer may be a patch embedding layer that takes each segmented point cloud data as input and outputs each segmented point cloud data vector. Each segmented point cloud data vector may represent a point cloud patch.

[0067] Sub-step three: Input each segmented point cloud data vector into the encoding layer to obtain extracted point cloud feature information corresponding to each segmented point cloud data vector. The encoding layer may be a Transformer Encoder layer that takes each segmented point cloud data vector as input and outputs each extracted point cloud feature information. Each of the extracted point cloud feature information may be a feature vector obtained by performing feature extraction on the segmented point cloud data vector.

[0068] Sub-step 4: Input the extracted point cloud feature information into the first feature fusion layer to obtain fused feature extraction information. The first feature fusion layer may be a feature fusion layer (FFL) that takes the extracted point cloud feature information as input and outputs the fused feature extraction information. The fused feature extraction information may be a feature vector obtained by concatenating the extracted point cloud feature information.

[0069] Sub-step 5: Input each segmented point cloud data vector into the position encoding layer to obtain position encoding information. The position encoding information may be a position encoding vector containing both absolute position information and relative position information. The absolute position information may be the spatial coordinates of a data point in the segmented point cloud data. The relative position information may represent the relative positional relationships between the data points in the segmented point cloud data.

[0070] Sub-step six: Input the fused extracted feature information and the position coding information into the second feature fusion layer to obtain target fused feature extraction information. The second feature fusion layer may be a feature fusion layer (FFL) that takes the fused extracted feature information and the position coding information as input and outputs the target fused feature extraction information. The target fused feature extraction information may be a vector formed by fusing the fused extracted feature information with the position coding information.

[0071] In sub-step seven, the target fusion feature extraction information is input into the pilling level detection layer to obtain detected pilling level information for the corresponding textile point cloud training sample. The pilling level detection layer may be a fully connected layer that takes the target fusion feature extraction information as input and outputs the detected pilling level information. The detected pilling level information may be the predicted pilling level information for the corresponding textile point cloud training sample. The pilling level information may indicate the degree of pilling on the textile.

[0072] In the second sub-step, the target pilling level information corresponding to each textile point cloud training sample in the textile point cloud training sample set is compared with the detected pilling level information corresponding to the textile point cloud training sample. In practice, the execution subject can use a cross-entropy loss function to compare the target pilling level information and the corresponding detected pilling level information to determine the difference between the target pilling level information and the detected pilling level information.

[0073] In a third sub-step, in response to determining that the to-be-trained textile pilling level detection model reaches the optimization target, the to-be-trained textile pilling level detection model is used as the trained textile pilling level detection model.

[0074] In a fourth sub-step, in response to determining that the training textile pilling level detection model has not achieved the optimization goal, the network parameters of the training textile pilling level detection model are adjusted, and unused textile point cloud training samples are used to form a textile point cloud training sample set. The adjusted training textile pilling level detection model is used as the training textile pilling level detection model, and the second training step is performed again. As an example, a back propagation algorithm (BP algorithm) can be used to adjust the network parameters of the training textile pilling level detection model.

[0075] Step 107 : determining the trained textile pilling level detection model to be trained as the textile pilling level detection model.

[0076] In some embodiments, the execution entity may determine the trained textile pilling level detection model to be trained as the textile pilling level detection model.

[0077] Step 108 : collecting three-dimensional point cloud data of the textile to be inspected as point cloud data of the textile to be inspected.

[0078] In some embodiments, the execution entity may collect three-dimensional point cloud data of the textile to be inspected as the textile point cloud data to be inspected, wherein the textile point cloud data to be inspected includes information about individual data points, and each of the data points includes coordinate position information and color information. In practice, the execution entity may collect the three-dimensional point cloud data of the textile to be inspected as the textile point cloud data to be inspected using a linear array laser 3D camera. The coordinate position information may represent the coordinates of a data point in the textile point cloud data to be inspected in three-dimensional space.

[0079] Step 109 : Segment the point cloud data of the textile to be inspected to obtain a point cloud block information set.

[0080] In some embodiments, the execution entity may segment the point cloud data of the textile to be inspected to obtain a point cloud block information set, wherein each point cloud block in the point cloud block information set includes information about each data point.

[0081] In some optional implementations of some embodiments, the execution entity may segment the point cloud data of the textile to be inspected through the following steps to obtain a point cloud block information set:

[0082] In the first step, each data point information included in the above-mentioned textile point cloud data to be detected is determined as a data point information set to be divided.

[0083] In the second step, based on the information set of the data points to be divided, the following segmentation steps are performed:

[0084] The first sub-step is to select one data point to be divided from the data point information set as the central data point information. In practice, the execution subject may randomly select one data point to be divided from the data point information set as the central data point information.

[0085] The second sub-step is to determine the distance between the spatial position corresponding to each of the data points to be divided, excluding the central data point information, in the data point information set to be divided, and the spatial position corresponding to the central data point information. The distance may be a Euclidean distance. In practice, the execution entity may determine the Euclidean distance between the spatial position corresponding to the coordinate position information included in the data point information to be divided and the spatial position corresponding to the coordinate position information included in the central data point information.

[0086] The third sub-step is to determine the determined distances as a distance set to be screened.

[0087] The fourth sub-step is to determine each distance in the to-be-screened distance set that satisfies a preset screening condition as each target distance, wherein the preset screening condition may be that the screening distance is less than or equal to a preset distance.

[0088] In the fifth sub-step, each piece of data point information to be divided corresponding to each target distance in the data point information set to be divided is determined as point cloud block information.

[0089] The sixth sub-step is to delete each piece of information of the data points to be divided from the information set of the data points to be divided, so as to update the information set of the data points to be divided.

[0090] In the third step, in response to determining that the updated information set of the data points to be divided is not empty, the segmentation step is performed again according to the updated information set of the data points to be divided.

[0091] In the fourth step, in response to determining that the updated information set of data points to be divided is empty, the determined information of each point cloud block is determined as a point cloud block information set.

[0092] The above technical solution and its related contents, as an inventive feature of the embodiments of the present disclosure, address the problem that "segmenting point cloud data using an octree algorithm to obtain individual point cloud blocks typically requires additional storage space to store the tree nodes and pointer information during the segmentation process. The tree structure itself may consume a large amount of memory space, resulting in a waste of computer storage resources." Factors that often lead to this waste of computer storage resources are as follows: Segmenting point cloud data using an octree algorithm to obtain individual point cloud blocks typically requires additional storage space to store the tree nodes and pointer information during the segmentation process. The tree structure itself may consume a large amount of memory space, resulting in a waste of computer storage resources. If these factors are addressed, the waste of computer storage resources can be reduced. To achieve this, first, the information on each data point included in the textile point cloud data to be inspected is determined as a data point information set to be segmented. Then, based on the data point information set to be segmented, the following segmentation steps are performed: In the first substep, information on a data point to be segmented is selected from the data point information set to be segmented as the center data point information. This allows the center data point information corresponding to the center position of a point cloud data block to be obtained. The second sub-step is to determine the distance between the spatial position corresponding to each of the data point information to be divided in the data point information set to be divided, except for the central data point information, and the spatial position corresponding to the central data point information. The third sub-step is to determine the determined distances as the distance set to be filtered. In this way, a distance set to be filtered for determining each target distance can be obtained. The fourth sub-step is to determine the distances that meet the preset filtering conditions in the distance set to be filtered as the target distances. In this way, each target distance for generating point cloud block information can be obtained. The fifth sub-step is to determine the data point information to be divided corresponding to each target distance in the data point information set to be divided as point cloud block information. In this way, point cloud block information representing the point cloud block after division can be obtained. The sixth sub-step is to delete the data point information to be divided from the data point information set to be divided, so as to update the data point information set to be divided. In this way, the data point information to be divided that has been divided can be deleted from the data point information set to be divided. Afterwards, in response to determining that the updated set of data point information to be divided is not empty, the segmentation step is performed again based on the updated set of data point information to be divided. Next, in response to determining that the updated set of data point information to be divided is empty, the determined individual point cloud block information is determined as a point cloud block information set. Thus, a divided point cloud block information set can be obtained. Furthermore, because no additional storage space for storing nodes and pointers is created during the segmentation of the individual data point information included in the textile point cloud data to be inspected, the segmentation process does not require the use of nodes and pointers to divide the textile point cloud data into blocks, thereby reducing waste of computer storage resources.

[0093] Step 110 : For each point cloud block in the point cloud block information set, input the point cloud block information into a textile pilling level detection model to obtain textile local pilling level information corresponding to the point cloud block information.

[0094] In some embodiments, the execution entity may input each point cloud block in the point cloud block information set into the textile pilling level detection model to obtain localized pilling level information of the textile corresponding to the point cloud block information. The localized pilling level information of the textile may be the pilling level information detected corresponding to the point cloud block information.

[0095] Step 111 : generating textile pilling level detection information corresponding to the textile to be detected based on the obtained local pilling level information of each textile.

[0096] In some embodiments, the execution entity may generate textile pilling level detection information corresponding to the textile to be detected based on the obtained local pilling level information of each textile.

[0097] In some optional implementations of some embodiments, the execution entity may generate the textile pilling level detection information corresponding to the textile to be detected based on the obtained local pilling level information of each textile through the following steps:

[0098] In the first step, each pilling level value included in the local pilling level information of each textile is determined as each target pilling level value. For example, the pilling level value may be 1.

[0099] The second step is to generate a textile pilling level value corresponding to the textile to be tested based on the target pilling level values. In practice, the execution entity may determine the average of the target pilling level values as the textile pilling level value corresponding to the textile to be tested.

[0100] The third step is to determine the textile pilling level value as the textile pilling level detection information.

[0101] The above-described embodiments of the present disclosure have the following beneficial effects: The textile pilling level detection methods of some embodiments of the present disclosure improve the accuracy of textile pilling level detection information and the stability of detection results. Specifically, the poor accuracy and stability of textile pilling level detection results are caused by the fact that when performing pilling level detection on textiles using two-dimensional images of the textile, two-dimensional images are significantly affected by environmental and viewing angle changes during imaging. For example, in dim lighting conditions, pilling may become more difficult to distinguish, resulting in poor accuracy of the textile pilling level detection information. Furthermore, changes in viewing angle may cause deformation or changes in surface texture of the textile in the image, which may cause pilling to appear different at different angles and may lead to unstable pilling level detection results. Based on this, the textile pilling level detection methods of some embodiments of the present disclosure first obtain an initial point cloud training sample set. This obtains the initial point cloud training sample set used to generate a target point cloud training sample set. Then, point cloud filtering is performed on the initial point cloud training sample set to obtain a target point cloud training sample set. This obtains the target point cloud training sample set. Next, the initial object recognition network undergoes a first training phase based on the target point cloud training sample set, obtaining a trained initial object recognition model. This allows the initial object recognition network to be trained using the target point cloud training sample set, improving its processing capability and adaptability to input point cloud data. The target point cloud training sample set during the first training phase helps the model form a smoother decision boundary, thereby reducing the risk of overfitting in the subsequent textile pilling level detection model to be trained. The trained initial object recognition model is then determined as the textile pilling level detection model to be trained. Next, a textile point cloud training sample set is obtained. This yields a textile point cloud training sample set for training the textile pilling level detection model to be trained. Next, a second training phase is performed on the textile pilling level detection model to be trained based on the textile point cloud training sample set, obtaining a trained textile pilling level detection model to be trained. The trained textile pilling level detection model to be trained is determined as the textile pilling level detection model. This yields a textile pilling level detection model for use in textile testing. Next, three-dimensional point cloud data of the textile to be inspected is collected as the textile point cloud data to be inspected. The textile point cloud data to be inspected includes information about individual data points, each of which includes coordinate position information and color information. This generates the three-dimensional point cloud data of the textile to be inspected. The textile point cloud data to be inspected is then segmented to generate a point cloud block information set, where each point cloud block in the point cloud block information set includes information about individual data points.Thus, a point cloud block information set for generating local pilling level information for each textile can be obtained. Next, for each point cloud block in the point cloud block information set, the point cloud block information is input into the textile pilling level detection model to obtain the local pilling level information for the textile corresponding to the point cloud block information. Thus, the point cloud block information set representing the three-dimensional point cloud data of the textile to be tested can be input into the textile pilling level detection model to perform local pilling detection and obtain local pilling level information for each textile. Finally, based on the obtained local pilling level information for each textile, textile pilling level detection information corresponding to the textile to be tested is generated. This is also because the textile pilling level detection model generates local pilling level information for each textile corresponding to the three-dimensional point cloud data of the textile to be tested. The three-dimensional point cloud data of the textile to be tested can not only capture the planar features of the textile surface, but also reflect its three-dimensional structure and subtle changes on the surface. In addition, the detection based on the three-dimensional point cloud data of the textile to be tested is less affected by changes in lighting and viewing angle, thereby improving the accuracy of the textile pilling level detection information and the stability of the detection results.

[0102] Further references Figure 2 As an implementation of the methods shown in the figures, the present disclosure provides some embodiments of a device for detecting the pilling level of textiles. These device embodiments are similar to Figure 1 Corresponding to the method embodiments shown, the device can be specifically applied to various electronic devices.

[0103] like Figure 2As shown, some embodiments of the textile pilling level detection device 200 include: a first acquisition unit 201, a point cloud filtering processing unit 202, a first training unit 203, a first determination unit 204, a second acquisition unit 205, a second training unit 206, a second determination unit 207, a collection unit 208, a segmentation processing unit 209, an input unit 210 and a generation unit 211. Among them, the first acquisition unit 201 is configured to acquire an initial point cloud training sample set; the point cloud filtering processing unit 202 is configured to perform point cloud filtering processing on the above-mentioned initial point cloud training sample set to obtain a target point cloud training sample set; the first training unit 203 is configured to perform a first training on the initial object recognition network according to the above-mentioned target point cloud training sample set to obtain a trained initial object recognition model; the first determination unit 204 is configured to determine the trained initial object recognition model as the to-be-trained textile pilling level detection model; the second acquisition unit 205 is configured to acquire a textile point cloud training sample set; the second training unit 206 is configured to perform a second training on the to-be-trained textile pilling level detection model according to the above-mentioned textile point cloud training sample set to obtain a trained textile pilling level detection model; the second determination unit 207 is configured to determine the trained textile pilling level detection model as the to-be-trained textile pilling level detection model. A fabric pilling level detection model; an acquisition unit 208 is configured to acquire three-dimensional point cloud data of the textile to be detected as the textile point cloud data to be detected, wherein the textile point cloud data to be detected includes information of each data point, and each data point information in the each data point information includes coordinate position information and color information; a segmentation processing unit 209 is configured to segment the textile point cloud data to obtain a point cloud block information set, wherein each point cloud block information in the point cloud block information set includes information of each data point; an input unit 210 is configured to input each point cloud block information in the point cloud block information set into the textile pilling level detection model to obtain local pilling level information of the textile corresponding to the point cloud block information; and a generation unit 211 is configured to generate textile pilling level detection information corresponding to the textile to be detected based on the obtained local pilling level information of each textile.

[0104] It is understood that the units described in the device 200 are similar to those described in the reference Figure 1 Therefore, the operations, features and beneficial effects described above for the method are also applicable to the device 200 and the units included therein, and will not be repeated here.

[0105] Reference below Figure 3 , which shows a structural diagram of an electronic device 300 suitable for implementing some embodiments of the present disclosure. Figure 3The electronic device shown is only an example and should not bring any limitation to the functions and scope of use of the embodiments of the present disclosure.

[0106] like Figure 3 As shown, the electronic device 300 may include a processing device (e.g., a central processing unit, a graphics processing unit, etc.) 301, which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) 302 or a program loaded from a storage device 308 into a random access memory (RAM) 303. Various programs and data required for the operation of the electronic device 300 are also stored in the RAM 303. The processing device 301, the ROM 302, and the RAM 303 are connected to each other via a bus 304. An input / output (I / O) interface 305 is also connected to the bus 304.

[0107] Typically, the following devices may be connected to the I / O interface 305: an input device 306 including, for example, a touch screen, a touchpad, a keyboard, a mouse, a camera, a microphone, an accelerometer, a gyroscope, etc.; an output device 307 including, for example, a liquid crystal display (LCD), a speaker, a vibrator, etc.; a storage device 308 including, for example, a magnetic tape, a hard disk, etc.; and a communication device 309. The communication device 309 may allow the electronic device 300 to communicate with other devices wirelessly or by wire to exchange data. Figure 3 The electronic device 300 is shown with various devices, but it should be understood that it is not required to implement or possess all of the devices shown. More or fewer devices may be implemented or possessed instead. Figure 3 Each block shown in the figure may represent one device, or may represent multiple devices as needed.

[0108] In particular, according to some embodiments of the present disclosure, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, some embodiments of the present disclosure include a computer program product comprising a computer program carried on a computer-readable medium, the computer program comprising program code for executing the method shown in the flowchart. In some such embodiments, the computer program can be downloaded and installed from a network via the communication device 309, or installed from the storage device 308, or installed from the ROM 302. When the computer program is executed by the processing device 301, the functions defined in the methods of some embodiments of the present disclosure are performed.

[0109] It should be noted that the computer-readable medium described in some embodiments of the present disclosure may be a computer-readable signal medium or a computer-readable storage medium or any combination of the two. The computer-readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device, or device, or any combination of the above. More specific examples of computer-readable storage media may include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination thereof. In some embodiments of the present disclosure, the computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, device, or device. In some embodiments of the present disclosure, the computer-readable signal medium may include a data signal propagated in baseband or as part of a carrier wave, which carries computer-readable program code. Such a propagated data signal may take a variety of forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. A computer-readable signal medium may also be any computer-readable medium other than a computer-readable storage medium that can transmit, propagate, or transfer a program for use by or in conjunction with an instruction execution system, apparatus, or device. The program code contained on the computer-readable medium may be transmitted using any suitable medium, including but not limited to wires, optical cables, RF (radio frequency), etc., or any suitable combination thereof.

[0110] In some embodiments, the client and server can communicate using any currently known or future developed network protocol, such as HTTP (HyperText Transfer Protocol), and can be interconnected with any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include a local area network ("LAN"), a wide area network ("WAN"), an internet (e.g., the Internet), and a peer-to-peer network (e.g., an ad hoc peer-to-peer network), as well as any currently known or future developed network.

[0111] The computer-readable medium may be included in the electronic device; or it may exist independently without being assembled into the electronic device. The computer-readable medium carries one or more programs. When the one or more programs are executed by the electronic device, the electronic device: obtains an initial point cloud training sample set; performs point cloud filtering processing on the initial point cloud training sample set to obtain a target point cloud training sample set; performs a first training on the initial object recognition network based on the target point cloud training sample set to obtain a trained initial object recognition model; determines the trained initial object recognition model as the to-be-trained textile pilling level detection model; obtains a textile point cloud training sample set; performs a second training on the to-be-trained textile pilling level detection model based on the textile point cloud training sample set to obtain a trained textile pilling level detection model; determines the trained textile pilling level detection model as the to-be-trained textile A pilling level detection model; collecting three-dimensional point cloud data of a textile to be detected as the textile point cloud data to be detected, wherein the textile point cloud data to be detected includes information of each data point, and each data point information in the each data point information includes coordinate position information and color information; segmenting the textile point cloud data to be detected to obtain a point cloud block information set, wherein each point cloud block information in the point cloud block information set includes information of each data point; for each point cloud block information in the point cloud block information set, inputting the point cloud block information into the textile pilling level detection model to obtain local pilling level information of the textile corresponding to the point cloud block information; and generating textile pilling level detection information corresponding to the textile to be detected based on the obtained local pilling level information of each textile.

[0112] Computer program code for performing the operations of some embodiments of the present disclosure may be written in one or more programming languages, or a combination thereof, including object-oriented programming languages such as Java, Smalltalk, C++, and conventional procedural programming languages such as "C" or similar programming languages. The program code may be executed entirely on the user's computer, partially on the user's computer, as a stand-alone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving a remote computer, the remote computer may be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., through the Internet using an Internet service provider).

[0113] The flowcharts and block diagrams in the accompanying drawings illustrate the possible implementation architecture, functions and operations of the systems, methods and computer program products according to various embodiments of the present disclosure. In this regard, each box in the flowchart or block diagram can represent a module, program segment, or a part of code, and the module, program segment, or a part of code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in a different order than that marked in the accompanying drawings. For example, two boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram and / or flowchart, and the combination of the boxes in the block diagram and / or flowchart, can be implemented with a dedicated hardware-based system that performs the specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0114] The units described in some embodiments of the present disclosure may be implemented in software or hardware. The units described may also be provided in a processor. For example, they may be described as follows: a processor comprising a first acquisition unit, a point cloud filtering processing unit, a first training unit, a first determination unit, a second acquisition unit, a second training unit, a second determination unit, an acquisition unit, a segmentation processing unit, an input unit, and a generation unit. The names of these units do not, in some cases, limit the units themselves. For example, the generation unit may also be described as "a unit for generating textile pilling level detection information corresponding to the textile to be detected based on the obtained local pilling level information of each textile."

[0115] The functions described above herein may be performed, at least in part, by one or more hardware logic components. For example, and without limitation, exemplary types of hardware logic components that may be used include: field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on chip (SOCs), complex programmable logic devices (CPLDs), and the like.

[0116] The above descriptions are merely some preferred embodiments of the present disclosure and an illustration of the technical principles employed. Those skilled in the art should understand that the scope of the invention involved in the embodiments of the present disclosure is not limited to the technical solutions formed by a specific combination of technical features, but should also encompass other technical solutions formed by any combination of technical features or their equivalents without departing from the inventive concept. For example, a technical solution formed by replacing a feature with a technical feature having similar functions as disclosed in the embodiments of the present disclosure (but not limited to) can be formed.

Claims

1. A method for detecting the pilling level of textiles, comprising: Obtain the initial point cloud training sample set; Performing point cloud filtering processing on the initial point cloud training sample set to obtain a target point cloud training sample set; Performing a first training on the initial object recognition network based on the target point cloud training sample set to obtain a trained initial object recognition model; The trained initial object recognition model is determined as the textile pilling level detection model to be trained; Obtain a textile point cloud training sample set; performing a second training on the to-be-trained textile pilling level detection model based on the textile point cloud training sample set to obtain a trained to-be-trained textile pilling level detection model; Determine the trained textile pilling level detection model as the textile pilling level detection model; Collecting three-dimensional point cloud data of the textile to be inspected as point cloud data of the textile to be inspected, wherein the point cloud data of the textile to be inspected includes information of each data point, and each data point in the information of each data point includes coordinate position information and color information; Segmenting the point cloud data of the textile to be inspected to obtain a point cloud block information set, wherein each point cloud block information in the point cloud block information set includes information of each data point; For each point cloud block information in the point cloud block information set, inputting the point cloud block information into the textile pilling level detection model to obtain textile local pilling level information corresponding to the point cloud block information; Based on the obtained local pilling level information of each textile, textile pilling level detection information corresponding to the textile to be detected is generated.

2. The method according to claim 1, wherein The target point cloud training samples in the target point cloud training sample set include sample point cloud data and sample target object identifiers corresponding to the sample point cloud data; The first step of training the initial object recognition network based on the target point cloud training sample set to obtain a trained initial object recognition model includes: The following first training step is performed based on the target point cloud training sample set: Inputting sample point cloud data of at least one target point cloud training sample in the target point cloud training sample set into the initial object recognition network to obtain an object recognition identifier corresponding to each target point cloud training sample in the at least one target point cloud training sample; comparing a sample target item identifier corresponding to each target point cloud training sample in at least one target point cloud training sample with an item identification identifier corresponding to the target point cloud training sample; Determine whether the initial object recognition network achieves the preset optimization goal based on the comparison results; In response to determining that the initial object recognition network achieves the optimization goal, using the initial object recognition network as a trained initial object recognition model; In response to determining that the initial object recognition network does not achieve the optimization goal, network parameters of the initial object recognition network are adjusted, and unused target point cloud training samples are used to form a target point cloud training sample set, and the adjusted initial object recognition network is used as the initial object recognition network to perform the first training step again.

3. The method according to claim 1, wherein The to-be-trained textile pilling level detection model comprises a point cloud data input layer, a point cloud embedding layer, an encoding layer, a first feature fusion layer, a position encoding layer, a second feature fusion layer, and a pilling level detection layer. Each textile point cloud training sample in the textile point cloud training sample set comprises sample textile point cloud data and sample target pilling level information corresponding to the sample textile point cloud data. And the second training of the to-be-trained textile pilling level detection model is performed based on the textile point cloud training sample set to obtain a trained to-be-trained textile pilling level detection model, comprising: The following second training step is performed based on the textile point cloud training sample set: For each textile point cloud training sample in the textile point cloud training sample set, perform the following steps: Inputting the sample textile point cloud data included in the textile point cloud training sample into the point cloud data input layer to obtain each divided point cloud data; Inputting each divided point cloud data into the point cloud embedding layer to obtain each divided point cloud data vector corresponding to each divided point cloud data; Inputting each divided point cloud data vector into the encoding layer to obtain each extracted point cloud feature information corresponding to each divided point data vector; Inputting each extracted point cloud feature information into the first feature fusion layer to obtain fused feature extraction information; Inputting each divided point cloud data vector into the position encoding layer to obtain position encoding information; Inputting the fused extracted feature information and the position encoding information into the second feature fusion layer to obtain target fused feature extraction information; Inputting the target fusion feature extraction information into the pilling level detection layer to obtain the pilling level information corresponding to the textile point cloud training sample; Compare the sample target pilling level information corresponding to each textile point cloud training sample in the textile point cloud training sample set with the detected pilling level information corresponding to the textile point cloud training sample; In response to determining that the to-be-trained textile pilling level detection model reaches the optimization goal, the to-be-trained textile pilling level detection model is used as the trained textile pilling level detection model to-be-trained; In response to determining that the textile pilling level detection model to be trained has not achieved the optimization goal, the network parameters of the textile pilling level detection model to be trained are adjusted, and unused textile point cloud training samples are used to form a textile point cloud training sample set. The adjusted textile pilling level detection model to be trained is used as the textile pilling level detection model to be trained, and the second training step is performed again.

4. The method according to claim 1, wherein Generating textile pilling level detection information corresponding to the textile to be detected based on the obtained local pilling level information of each textile includes: Determining the respective pilling level values included in the respective local pilling level information of the textiles as respective target pilling level values; Generating a textile pilling level value corresponding to the textile to be tested based on the target pilling level values; The textile pilling level value is determined as textile pilling level detection information.

5. The method according to claim 1, wherein The method further comprises: Controlling the associated printing device to print the obtained textile pilling level detection information onto label paper; The associated mechanical arm is controlled to stick the label paper onto the textile to be inspected.

6. A device for detecting the pilling level of textiles, comprising: A first acquisition unit is configured to acquire an initial point cloud training sample set; a point cloud filtering processing unit, configured to perform point cloud filtering processing on the initial point cloud training sample set to obtain a target point cloud training sample set; A first training unit is configured to perform a first training on the initial object recognition network based on the target point cloud training sample set to obtain a trained initial object recognition model; A first determining unit is configured to determine the trained initial object recognition model as a to-be-trained textile pilling level detection model; a second acquisition unit, configured to acquire a textile point cloud training sample set; A second training unit is configured to perform a second training on the to-be-trained textile pilling level detection model based on the textile point cloud training sample set to obtain a trained to-be-trained textile pilling level detection model; A second determining unit is configured to determine the trained textile pilling level detection model to be trained as the textile pilling level detection model; an acquisition unit configured to acquire three-dimensional point cloud data of the textile to be inspected as point cloud data of the textile to be inspected, wherein the point cloud data of the textile to be inspected includes information of each data point, and each data point in the information of each data point includes coordinate position information and color information; a segmentation processing unit configured to perform segmentation processing on the point cloud data of the textile to be inspected to obtain a point cloud block information set, wherein each point cloud block information in the point cloud block information set includes information of each data point; An input unit is configured to input each point cloud block information in the point cloud block information set into the textile pilling level detection model to obtain textile local pilling level information corresponding to the point cloud block information; The generating unit is configured to generate textile pilling level detection information corresponding to the textile to be detected based on the obtained local pilling level information of each textile.

7. An electronic device comprising: one or more processors; a storage device having one or more programs stored thereon; When the one or more programs are executed by the one or more processors, the one or more processors implement the method according to any one of claims 1 to 5.

8. A computer-readable medium having a computer program stored thereon, wherein: When the program is executed by a processor, the method according to any one of claims 1 to 5 is implemented.

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