Method for processing wound yarn package data, processing apparatus, electronic device, storage medium, and program

Abstract

To provide a method, an apparatus, an electronic device, a storage medium, and a program for processing wound yarn package data.SOLUTION: A method includes: performing, if it is determined that a conveyed wound yarn package has entered a detection area, defect detection on the wound yarn package located within the detection area, to obtain a target detection result for the wound yarn package, wherein the target detection result is used to indicate the degree of defect in the wound yarn package; and obtaining, if it is determined that the target detection result satisfies preset defect requirements, a target grade of the wound yarn package based on the target detection result of the wound yarn package and the preset grade of the wound yarn package.SELECTED DRAWING: Figure 1

Description

[Technical Field]

[0001] The present disclosure relates to the field of data processing, and more particularly to a method, apparatus, electronic device, storage medium, and program for processing wound yarn package data. [Background technology]

[0002] In the chemical fiber industry, workers usually have to inspect one ball of yarn package for defects and then reassess the grade of the yarn package based on the inspection results. This inspection method relies heavily on human experience and is inefficient, which affects the production and management efficiency of yarn packages. Summary of the Invention [Problem to be solved by the invention]

[0003] The present disclosure provides a method, apparatus, electronic device, storage medium, and program for processing wound yarn package data to solve or alleviate one or more technical problems in the prior art. [Means for solving the problem]

[0004] In a first aspect, the present disclosure provides a method for processing wound yarn package data, the method comprising: When it is determined that the transported wound yarn package has entered a detection area, a defect detection is performed on the wound yarn package located within the detection area to obtain a target detection result for the wound yarn package, and the target detection result is used to indicate the degree of the defect in the wound yarn package; If it is determined that the target detection result satisfies the preset defect requirement, obtaining a target grade for the wound yarn package based on the target detection result for the wound yarn package and the preset grade for the wound yarn package.

[0005] In a second aspect, the present disclosure provides an apparatus for processing wound yarn package data, the apparatus comprising: a defect detection unit for detecting defects in the wound yarn package located within the detection area when it is determined that the wound yarn package being conveyed has entered the detection area, and obtaining a target detection result for the wound yarn package, the target detection result being used to indicate the degree of a defect in the wound yarn package; and a grade determination unit for determining a target grade of the wound yarn package based on the target detection result of the wound yarn package and a preset grade of the wound yarn package when it is determined that the target detection result satisfies the preset defect requirement.

[0006] In a third aspect, the present disclosure provides an electronic device, the device comprising: at least one processor; a memory communicatively coupled to the at least one processor; The memory stores instructions executable by the at least one processor, which, when executed by the at least one processor, cause the implementation of any one of the methods in the embodiments of the present disclosure.

[0007] In a fourth aspect, there is provided a non-transitory computer-readable storage medium having stored thereon computer instructions for causing a computer to perform any one of the methods in the embodiments of the present disclosure.

[0008] In a fifth aspect, a program is provided, which, when executed by a processor, implements any one of the methods in the embodiments of the present disclosure. [Effects of the Invention]

[0009] The beneficial effects of the solution provided by the present disclosure include at least the following:

[0010] The beneficial effects of the technical solution provided by the present disclosure include at least the following: The technical solution of the present disclosure first performs automatic defect detection on the appearance of the yarn package, and then accurately evaluates the actual grade of the yarn package based on the actual defect situation of the yarn package (e.g., the target detection result), and then makes quick adjustments to the grade of the spindle, thereby realizing a fully automated processing process from yarn package defect detection to yarn package grade evaluation. Compared with traditional manual methods, the technical solution of the present disclosure does not need to rely on human experience, can efficiently detect defects in the yarn package, and can automatically adjust the grade of the yarn package based on the detection result, thereby saving a lot of labor and time costs and further improving the management efficiency of yarn packages. [Brief explanation of the drawings]

[0011] [Figure 1] 1 is a schematic flowchart of a method for processing wound yarn package data according to an embodiment of the present disclosure; [Figure 2] 10 is a second schematic flowchart of a method for processing wound yarn package data according to an embodiment of the present disclosure. [Figure 3] 1 is a schematic diagram illustrating a scenario of image collection of a winding package according to an embodiment of the present disclosure. [Figure 4] FIG. 2 is a schematic diagram illustrating a configuration of an example of a second network layer according to an embodiment of the present disclosure. [Figure 5] FIG. 10 is a schematic diagram illustrating an example of a configuration of a sixth sub-network layer in a second network layer according to an embodiment of the present disclosure. [Figure 6] FIG. 2 is a schematic diagram illustrating the configuration of a feature weight module according to an embodiment of the present disclosure. [Figure 7] FIG. 1 is a schematic diagram illustrating the configuration of a multi-view attention module according to one embodiment of the present disclosure. [Figure 8] 10 is a third schematic flowchart of a method for processing wound yarn package data according to an embodiment of the present disclosure. [Figure 9]1 is a schematic diagram illustrating a configuration of a processing device for wound yarn package data according to an embodiment of the present disclosure. [Figure 10] FIG. 1 is a block diagram of an electronic device for implementing a method for processing winding package data according to an embodiment of the present disclosure. DETAILED DESCRIPTION OF THE INVENTION

[0012] It should be understood that the contents described herein are not intended to describe key points or key features of the embodiments of the present disclosure, nor are they used to limit the scope of the present disclosure. Other features of the present disclosure will be understood through the following specification.

[0013] In the accompanying drawings, unless otherwise specified, the same reference numerals throughout the accompanying drawings indicate the same or similar components or elements. The accompanying drawings are not necessarily drawn to scale. It should be understood that the drawings illustrate only some examples provided by the present disclosure and should not be considered as limiting the scope of the present disclosure.

[0014] The present disclosure will now be described in more detail with reference to the accompanying drawings, in which like reference numerals represent like or similar elements and in which various aspects of the embodiments are shown, and which, unless otherwise noted, are not necessarily drawn to scale.

[0015] Furthermore, in order to better explain the present disclosure, many specific details are described in the following specific embodiments. Those skilled in the art should understand that the present disclosure can be similarly implemented without some details. In some examples, methods, means, components, circuits, etc. that are well known to those skilled in the art are not described in detail so that the gist of the present disclosure is clear.

[0016] The smallest product unit in the chemical fiber industry is a single ball of yarn package, and based on the data stream created by a single ball of yarn package, the entire yarn package production process for each ball has been made transparent.

[0017] A yarn winding data stream refers to assigning a code (e.g., a two-dimensional code or barcode) to each yarn winding package, and then linking the automatic doffing, yarn winding weighing, visual inspection, automatic packaging line, and yarn winding transport stages via a physical conveying device, so that relevant data for each yarn winding package can be immediately and completely collected and transmitted at each stage. However, the visual inspection process for each yarn winding package typically requires manual inspection of the yarn winding package and manual evaluation of the actual grade of the yarn winding package based on the inspection results. This inspection method is obviously inefficient and heavily reliant on human experience. Therefore, an automated processing process is needed to efficiently recognize defects in yarn winding packages and re-evaluate the grade of the yarn winding package.

[0018] Therefore, the present disclosure provides a method for processing winding package data to solve the above-mentioned problems.

[0019] 1 is a simplified flowchart of a method for processing yarn package data according to one embodiment of the present disclosure, the method being selectively applied to electronic devices such as personal computers, servers, server clusters, and the like.

[0020] Furthermore, the method includes at least part of the following: As shown in FIG.

[0021] In step S101, when it is determined that the conveyed wound yarn package has entered the detection area, defect detection is performed on the wound yarn package located within the detection area, and a target detection result for the wound yarn package is obtained.

[0022] Here, the target detection result is used to indicate the degree of defect in the wound yarn package.

[0023] Furthermore, in one embodiment, the main type of yarn in the wound yarn package can include one or more of partially oriented yarns (POY), fully drawn yarns (FDY), drawn textured yarns (DTY) (or low modulus filaments), etc. For example, the types of yarn in the wound yarn package can specifically include polyester partially oriented yarns, polyester fully drawn yarns, polyester drawn yarns, polyester drawn textured yarns, etc.

[0024] Furthermore, in one specific example, defect detection for a wound yarn package can specifically refer to detecting defects such as broken yarn, fuzz, oil stains, tangles, nips, paper tube damage, thread hooking, and incorrect paper tube color.

[0025] In step S102, if it is determined that the target detection result meets the preset defect requirements, a target grade of the wound yarn package is obtained based on the target detection result of the wound yarn package and the preset grade of the wound yarn package.

[0026] The preset grade of the yarn winding package may be the highest preset grade or another grade. For example, in a scenario where the yarn winding package needs to be downgraded, the preset grade of the yarn winding package is set to the highest grade, and in that case, it can be determined whether the preset grade of the yarn winding package needs to be downgraded based on the target detection result of the yarn winding package. In actual applications, the preset grade of the yarn winding package can be set according to the needs of the actual scenario, and the present disclosure does not specifically limit this.

[0027] In this way, the present disclosure can determine the target grade of the yarn package using the target detection results of the yarn package. Specifically, the present disclosure first performs automatic defect detection on the appearance of the yarn package, and then accurately evaluates the actual grade of the yarn package based on the actual defect situation of the yarn package (e.g., the target detection results), and then can quickly adjust the grade of the yarn package. In this way, a fully automatic processing process from defect detection of the yarn package to grade evaluation of the yarn package is realized. Compared to the traditional manual method, the method of the present disclosure can efficiently detect defects in the yarn package without relying on human experience, and can automatically adjust the grade of the yarn package based on the detection results, thereby saving a lot of labor and time costs and further improving the management efficiency of the yarn package.

[0028] Furthermore, the grade of the wound yarn package can be automatically adjusted, which effectively improves the data quality of the wound yarn data, better realizes quality control and feedback for the wound yarn package in the production process, and effectively avoids potential losses to the user.

[0029] 2 is a second schematic flowchart of a method for processing winding package data according to an embodiment of the present disclosure. This method can be selectively applied to electronic devices such as personal computers, servers, and server clusters. The relevant content of the method shown in FIG. 1 above can also be applied to this embodiment, and the relevant content will not be repeated in this embodiment.

[0030] Furthermore, the method includes at least part of the following: As shown in FIG.

[0031] In step S201, when it is determined that the conveyed wound yarn package has entered the detection area, N images of the wound yarn package are acquired.

[0032] Here, an image in the N images includes at least a partial area of the wound yarn package; for example, an image may include a local area such as the top surface, bottom surface, or annular surface (i.e., side surface) of the wound yarn package.

[0033] Furthermore, in one example, N is 3 or greater.

[0034] Furthermore, in one embodiment, images of the yarn package can be collected as follows. Specifically, step S201 can include, when receiving response information to the radio frequency identification corresponding to the yarn package (in this case, it is considered that the yarn package has entered the detection area), using N image collection components to collect images of the yarn package at different angles to obtain N images. For example, as shown in FIG. 3 , three image collection components are used to collect images of the yarn package in all directions, i.e., image collection component 1 collects images of the top surface of the yarn package, image collection component 2 collects images of the left portion of the bottom surface of the yarn package and the left side of the yarn package, and image collection component 3 collects images of the right portion of the bottom surface of the yarn package and the right side of the yarn package. This helps to perform comprehensive defect detection on the yarn package and lays a foundation for improving the accuracy of defect detection on subsequent yarn packages.

[0035] Here, in one example, the image collection method of the winding package may be a method in which, when it is determined that the winding package has been transported and entered the detection area, the image collection component generates a control command to instruct a mechanical gripper to grasp and rotate the winding package in order to collect images of the winding package in all directions, or it may be another image collection method, and the present disclosure is not particularly limited to the image collection method of the winding package.

[0036] In step S202, the N images are input into a target detection model to obtain a first detection result corresponding to each image in the N images.

[0037] Here, the target detection model is used to recognize whether there is a defect in a local area of the wound yarn package and obtain a first detection result, which includes at least one of the number of defects, the defect location, and the defect type.

[0038] In step S203, a target detection result of the wound yarn package is obtained based on the first detection result corresponding to each image in the N images.

[0039] Here, the target detection result is used to indicate the degree of defect in the wound yarn package.

[0040] In step S204, it is determined whether the target detection result satisfies the preset defect requirement, and if so, the process proceeds to step S205, and if not, the process proceeds to step S206.

[0041] In step S205, if it is determined that the target detection result meets the preset defect requirements, a target grade of the wound yarn package is obtained based on the target detection result of the wound yarn package and the preset grade of the wound yarn package.

[0042] In step S206, if it is determined that the target detection result does not satisfy the preset defect requirement, the preset grade of the wound yarn package is set as the target grade of the wound yarn package.

[0043] In this way, the present disclosure uses a model to detect defects in a wound yarn package, and further determines a target grade for the wound yarn package based on the obtained detection results (e.g., target detection results), thereby accurately evaluating the actual grade of the wound yarn package, thereby realizing automatic adjustment of the grade of the wound yarn package and significantly improving the management efficiency of the wound yarn package.Furthermore, because the present disclosure uses a model to detect defects, it realizes efficient recognition of defects in the wound yarn package and significantly improves the evaluation accuracy of the grade of the wound yarn package.

[0044] Furthermore, in one example, the target detection model may be a dynamic weight-based wavelet attention neural network, or other models obtained by improving on Dynamic Wavelet Convolution Networks (DWCNet), and the present disclosure is not particularly limited thereto.

[0045] Furthermore, in one embodiment, the target detection model includes at least a first network layer, a second network layer, and a third network layer.

[0046] Specifically, the first network layer is used to perform feature processing on the input image to obtain a low-level feature map, for example, the first network layer may be a dynamic wavelet convolutional network, where the low-level feature map represents the feature map extracted after removing background noise in the image.

[0047] Furthermore, the second network layer is used to perform feature enhancement processing on at least key feature information in the low-level feature map to obtain a high-level feature map. For example, in one example, the second network layer can be specifically used to perform feature enhancement processing on key feature information in the low-level feature map, and accordingly suppress irrelevant feature information in the low-level feature map, such as suppressing feature information irrelevant to the yarn package, to obtain a high-level feature map.

[0048] Furthermore, the third network layer is used to perform defect recognition based on the high-level feature map to obtain a first detection result.

[0049] In this way, the present disclosure provides a specific example of a model for performing rapid defect detection on wound yarn packages. Thus, compared to conventional manual methods, the present disclosure can achieve efficient detection of defects in wound yarn packages without requiring human experience, and can particularly detect weak defects in wound yarn packages that are difficult to detect, thereby further improving the accuracy of detection and laying the foundation for subsequent rapid evaluation of the grade of the wound yarn package.

[0050] In a specific example of the present disclosure, the first network layer may include at least a first subnetwork layer, a second subnetwork layer, a third subnetwork layer, and a fourth subnetwork layer.

[0051] Here, the first sub-network layer is used to extract local features from the input image to obtain a low-frequency feature map, the second sub-network layer is used to obtain a target weighting factor for the feature map of the input image, the third sub-network layer is used to extract global features from the input image to obtain a global feature map (e.g., to obtain a global feature map including low-frequency information and high-frequency information), and the fourth sub-network layer is used to fuse the low-frequency feature map and the global feature map based on the target weighting factor to remove noise and obtain a low-level feature map. That is, the first network layer can combine the extracted local feature information (e.g., the low-frequency feature map) and global feature information (e.g., the global feature map) to obtain a higher-quality feature map, thereby better capturing defect feature information, thereby improving the accuracy of defect detection and laying the foundation for accurately evaluating the grade of wound yarn packages.

[0052] For example, in one example, the target weight coefficient determined by the second sub-network layer is w, the low-frequency feature map obtained by the first sub-network layer is p, the global feature map obtained by the third sub-network layer is q, and the low-level feature map obtained by the fourth sub-network layer is x, and the specific expression is as follows:

[0053]

number

[0054] In this way, the present disclosure can utilize the obtained weighting coefficients to appropriately combine the obtained local feature information and global feature information, and thus adjust the noise present in the feature map, which helps to mitigate the interference of noise on defect detection and further improve the accuracy of defect detection.

[0055] In another example of the present disclosure, the second network layer includes at least a fifth subnetwork layer, a sixth subnetwork layer, and a seventh subnetwork layer.

[0056] The fifth sub-network layer performs feature extraction on the low-level feature maps and fuses the extracted feature maps to obtain M initial fused feature maps, where M is an integer greater than or equal to 2. The sixth sub-network layer performs key feature extraction on each of the M initial fused feature maps and performs feature enhancement on the key feature information extracted from each initial fused feature map to obtain M target-enhanced feature maps. The seventh sub-network layer fuses the obtained M target-enhanced feature maps to obtain a high-level feature map. In this way, clear and abundant feature information (e.g., a high-level feature map) can be obtained, further improving the accuracy of defect detection, especially for weak defects that are difficult to detect in wound yarn packages, thereby significantly improving the accuracy of weak defect detection and laying the foundation for subsequent accurate grade evaluation of wound yarn packages.

[0057] For example, the fifth sub-network layer is a feature pyramid network (FPN) layer, the sixth sub-network layer is a feature enhancement network layer, and the seventh sub-network layer is a linear addition (Add) processing layer. As shown in FIG. 4, first, low-level feature maps are input to the FPN layer to obtain M initial fusion feature maps. Next, each of the obtained initial fusion feature maps is input to the feature enhancement network layer. For example, key feature extraction is performed on each initial fusion feature map through the feature enhancement network layer, and feature enhancement processing is performed on the extracted feature maps to obtain M target enhancement feature maps. Finally, the obtained M target enhancement feature maps are input to the linear addition processing layer to obtain high-level feature maps. This helps improve the accuracy of defect and weak defect detection and provides a basis for accurately evaluating the grade of the subsequent wound yarn package.

[0058] Here, in one example, the i-th target-enhanced feature map among the M target-enhanced feature maps is obtained based on the following method.

[0059] The i-th initial fusion feature map is convolved to obtain the i-th weighting factor based on the result of the convolution process, the i+1-th initial fusion feature map and the i-th initial fusion feature map are fused based on the i-th weighting factor to obtain the i-th target fusion feature map, key features are extracted from the obtained i-th target fusion feature map, feature enhancement is performed on each of the extracted feature maps (e.g., enhancement to a corresponding degree for different feature maps) to obtain multiple i-th initial enhanced feature maps, and the multiple i-th initial enhanced feature maps are fused to obtain the i-th target enhanced feature map, where i is an integer between 1 and M-1.

[0060] Furthermore, when i is M, the (M+1)th initial fused feature map may be a preset value, in which case the (M)th target fused feature map is obtained, and then the (M)th target enhanced feature map is obtained. Alternatively, the (M)th target enhanced feature map may be obtained as follows:

[0061] Key features are extracted from the Mth initial fusion feature map, and an enhancement process is performed on each of the extracted feature maps to obtain multiple Mth initial enhanced feature maps, and the multiple Mth initial enhanced feature maps are then fused to obtain the Mth target enhanced feature map.

[0062] For example, continue to take the sixth sub-network layer as a feature enhancement network layer. In this case, the feature enhancement network layer may further specifically include a feature weight module and a multi-view attention module. As shown in FIG. 5, among the M initial fusion feature maps, the i-th initial fusion feature map and the (i+1)-th initial fusion feature map are input to the feature weight module to obtain the i-th target fusion feature map. The obtained i-th target fusion feature map is input to the multi-view attention module for key feature extraction and feature enhancement processing to obtain the i-th target enhanced feature map. In addition, for the M-th initial fusion feature map, the M-th initial fusion feature map is directly used as the M-th target fusion feature map. The obtained M-th target fusion feature map is input to the multi-view attention module for key feature extraction and feature enhancement processing to obtain the M-th target enhanced feature map.

[0063] Furthermore, as shown in Figure 6, the feature weight module first performs upsampling on the (i+1)th initial fusion feature map to obtain the processed (i+1)th initial fusion feature map, then performs convolution on the i-th initial fusion feature map, and performs S-shaped bending function (Sigmod) on the result obtained after the processing to obtain the i-th weight coefficient (e.g., λ in the figure). i , or 1-λ i ), and then the i-th weight coefficient, e.g., 1-λ iand perform element-by-element multiplication on the i-th initial fused feature map, λ i Then, perform element-by-element multiplication on the (i+1)th initial fusion feature map processed by [1], and perform element-by-element addition on the obtained result to obtain the i-th target fusion feature map. Here, if i is M, the M-th initial fusion feature map does not need to be input to the feature weight module, and can be directly used as the M-th target fusion feature map.

[0064] Furthermore, as shown in Figure 7, the multi-view attention module first performs a deformable convolution process using a first preset convolution kernel (e.g., 1x1) on the i-th target fusion feature map to extract key features, thereby obtaining key feature feature map 1; then performs a deformable convolution process using a second preset convolution kernel (e.g., 3x3) on the i-th target fusion feature map to obtain key feature feature map 2; and then performs a deformable convolution process using a third preset convolution kernel (e.g., 5x5) on the i-th target fusion feature map to obtain key feature feature map 3. Next, the obtained feature map 1 is input to attention submodule A to perform feature enhancement on feature map 1 to obtain initial enhanced feature map 1; similarly, feature map 2 is input to attention submodule B to obtain initial enhanced feature map 2; and feature map 3 is input to attention submodule C to obtain initial enhanced feature map 3; and finally, the three i-th initial enhanced feature maps are fused (e.g., summed) to obtain the i-th target enhanced feature map.

[0065] In addition to what is shown in FIG. 7, a neural network can be added, for example, a wavelet neural network, and the wavelet neural network can be used to directly extract features from the i-th target fusion feature map to obtain the i-th supplementary feature map, which can then be fused together with the three i-th initial reinforced feature maps.

[0066] Note that the structure of the attention submodule (e.g., attention submodule A, attention submodule B, or attention submodule C) included in the multi-view attention module may be a squeeze and excitation (SE) network, or other network structure obtained based on an SE network, and the present disclosure is not limited thereto.

[0067] In this way, the present disclosure can fully utilize the sixth sub-network layer to extract key features in the low-level feature map, and then perform feature enhancement processing on the extracted results, thereby guiding the network's attention to potential targets (i.e., defects in the wound yarn package) and helping to better recognize the defects in the subsequent process. In particular, for weak defects in the wound yarn package that are difficult to detect, key features of the weak defects can be obtained more comprehensively, further improving the detection accuracy of weak defects and laying the foundation for subsequent accurate grading of the wound yarn package.

[0068] In a further example of the present disclosure, the third network layer includes an eighth sub-network layer and a ninth sub-network layer.

[0069] The eighth sub-network layer is used to frame candidate regions for false defects in the image based on the high-level feature map, and the ninth sub-network layer is used to recognize whether a defect exists in the candidate region based on the high-level feature map and the frame-selected candidate region. In this way, possible defects in the wound yarn package can be efficiently recognized, laying the foundation for subsequent accurate evaluation of the grade of the wound yarn package and automatic adjustment.

[0070] 8 is a schematic flowchart of a method for processing winding package data according to an embodiment of the present disclosure. This method can be selectively applied to electronic devices such as personal computers, servers, and server clusters. The relevant content of the methods shown in FIGS. 1 and 2 above can also be applied to this embodiment, and the relevant content will not be repeated in this embodiment.

[0071] Furthermore, the method includes at least part of the following content: As shown in FIG.

[0072] In step S801, if it is determined that the transported wound yarn package has entered the detection area, N images of the wound yarn package are acquired, where each of the N images includes at least a portion of the wound yarn package, and N is 3 or greater.

[0073] In step S802, the N images are input into a target detection model to obtain a first detection result corresponding to each image in the N images.

[0074] Here, the target detection model is used to recognize whether defects exist in a local area of the wound yarn package and obtain a first detection result, which includes at least one of the number of defects, defect locations, and defect types.

[0075] In step S803, information regarding the defect in the wound yarn package is obtained, and at least one of the following is performed to determine a target detection result representing the degree of the defect in the wound yarn package based on the information regarding the defect in the wound yarn package. Method 1: The number of defects contained in each of the N first detection results is counted to obtain the total number of defects in the wound yarn package. Method 2: Based on the defect locations (e.g., including the location and defect size) contained in each of the N first detection results, obtain all defect locations (e.g., obtain the overall defect size) in the wound yarn package. Method 3: Based on the defect type included in each of the N first detection results, a target defect type for the wound yarn package (e.g., this target defect type includes the defect types included in all of the first detection results) is obtained.

[0076] In step S804, it is determined whether the target detection result satisfies the preset defect requirement, and if so, the process proceeds to step S805, and if not, the process proceeds to step S806.

[0077] In step S805, if it is determined that the target detection result meets the preset defect requirements, a target grade of the wound yarn package is obtained based on the target detection result of the wound yarn package and the preset grade of the wound yarn package.

[0078] Furthermore, in one embodiment, the target grade of the wound yarn package can be obtained by the following method, which can automatically adjust the grade of the wound yarn package, further improving the management efficiency of the wound yarn package and ensuring the data quality of the wound yarn data. Specifically, when it is determined that the target detection result satisfies the preset defect requirements, obtaining the target grade of the wound yarn package based on the target detection result of the wound yarn package and the preset grade of the wound yarn package (for example, the above step S805) can be specifically performed as follows: If it is determined that at least one of the following conditions is met, the preset grade of the wound yarn package is adjusted downgraded to obtain the target grade of the detected wound yarn package.

[0079] Condition 1: The total number of defects in the wound yarn package in the target detection result is greater than a preset threshold value.

[0080] Condition 2: All defect positions in the wound yarn package in the target detection result are within a predetermined defect position range (e.g., the overall defect size is within the predetermined defect position range and exceeds a predetermined area).

[0081] Condition 3: The target defect type of the wound yarn package in the target detection result is within the range of preset defect types (for example, if the target defect type includes defect types included in all first detection results, at least one defect type is within the range of preset defect types, and then the target defect type can be considered to be within the range of preset defect types).

[0082] In step S806, if it is determined that the target detection result does not satisfy the preset defect requirement, the preset grade of the wound yarn package is set as the target grade of the wound yarn package.

[0083] In this way, the present disclosure can use a model to detect defects in a wound yarn package and determine the grade of the wound yarn package based on the detection result (e.g., the target detection result), thereby automatically adjusting the grade of the wound yarn package and greatly improving the management efficiency of the wound yarn package, thereby ensuring the data quality of the wound yarn data and effectively avoiding potential losses to users.

[0084] As can be seen from the above, the technical solution of the present disclosure has several advantages over the prior art, specifically including:

[0085] First, it realizes automatic processing. Compared with the traditional manual method, the method disclosed herein can efficiently detect defects in wound yarn packages without relying on human experience, and can automatically adjust the grade of the wound yarn package based on the detection results, thereby saving a large amount of labor and time costs and further improving the management efficiency of wound yarn packages.

[0086] Second, it improves detection accuracy. While traditional manual detection methods have difficulty detecting appearance defects in wound yarn packages and are prone to overlooking them, the solution disclosed herein uses a neural network model that is more suitable for defect detection, achieving higher detection accuracy, especially for weak defects that are difficult to detect, thereby ensuring the quality of wound yarn packages.

[0087] Third, improve the quality of wound yarn data. In the application process of wound yarn data streams, the solution disclosed herein can further improve the data quality of a wound yarn package when detecting appearance defects on a wound yarn package, thereby better realizing quality control and feedback for wound yarn packages in the production process and effectively avoiding potential losses to users.

[0088] The present disclosure further proposes a processing device for wound yarn package data, as shown in FIG. 9, which includes: a defect detection unit 901 for, when it is determined that a transported wound yarn package has entered a detection area, detecting a defect in the wound yarn package located within the detection area and obtaining a target detection result for the wound yarn package, the target detection result being used to indicate the degree of a defect in the wound yarn package; and a grade determination unit 902 for obtaining a target grade for the wound yarn package based on the target detection result of the wound yarn package and the preset grade of the wound yarn package when it is determined that the target detection result meets the preset defect requirements.

[0089] In one embodiment of the present disclosure, the defect detection unit 901 specifically includes: acquiring N images of the wound yarn package, each of the N images including at least a portion of the wound yarn package, where N is 3 or greater; inputting the N images into a target detection model to obtain a first detection result corresponding to each image in the N images, the target detection model being used to recognize whether a defect exists in a local area of the wound yarn package and obtain a first detection result, the first detection result including at least one of a defect number, a defect location, and a defect type; and obtaining a target detection result for the wound yarn package based on a first detection result corresponding to each image in the N number of images.

[0090] In one embodiment of the present disclosure, the defect detection unit 901 specifically includes: obtaining information about defects in the wound yarn package and determining a target detection result indicative of a degree of defect in the wound yarn package based on the information about the defects in the wound yarn package; calculating a total number of defects in the wound yarn package by calculating the number of defects in each of the N first detection results; obtaining all defect positions in the wound yarn package based on defect positions included in each of the N first detection results; and obtaining a target defect type for the wound yarn package based on the defect type included in each of the N first detection results.

[0091] In one embodiment of the present disclosure, the grade determination unit 902 specifically includes: the total number of defects in the wound yarn package in the target detection result is greater than a preset threshold value; and all defect positions of the wound yarn package in the target detection result are within a predetermined defect position range; If it is determined that at least one of the following conditions is met: the target defect type of the wound yarn package in the target detection result is within a range of preset defect types, the preset grade of the wound yarn package is downgraded to obtain the target grade of the detected wound yarn package.

[0092] In one embodiment of the present disclosure, the target detection model includes at least a first network layer, a second network layer, and a third network layer; The first network layer is used to perform feature processing on the input image to obtain a low-level feature map, and the low-level feature map is used to represent the feature map extracted after removing background noise in the image; The second network layer is used to perform feature enhancement processing on key feature information in at least the low-level feature map to obtain a high-level feature map; The third network layer is used to perform defect recognition based on the high-level feature map to obtain a first detection result.

[0093] In one embodiment of the present disclosure, the first network layer includes at least a first subnetwork layer, a second subnetwork layer, a third subnetwork layer, and a fourth subnetwork layer; The first sub-network layer is used to extract local features from the input image to obtain a low-frequency feature map; the second sub-network layer is used to obtain target weighting coefficients for the feature map of the input image; the third sub-network layer is used to extract global features from the input image to obtain a global feature map; and the fourth sub-network layer is used to merge the low-frequency feature map and the global feature map based on the target weighting coefficients to remove noise and obtain a low-level feature map.

[0094] In one embodiment of the present disclosure, the second network layer includes at least a fifth subnetwork layer, a sixth subnetwork layer, and a seventh subnetwork layer; The fifth sub-network layer is used to perform feature extraction on the low-level feature maps, and then fuse the extracted feature maps to obtain M initial fused feature maps, where M is an integer greater than or equal to 2; The sixth sub-network layer is used for extracting key features from each of the M initial fusion feature maps, and for enhancing the key feature information extracted from each of the M initial fusion feature maps to obtain M target enhanced feature maps; The seventh sub-network layer is used to fuse the obtained M target-enhanced feature maps to obtain a high-level feature map.

[0095] In one embodiment of the present disclosure, the i-th target-enhanced feature map among the M target-enhanced feature maps is When i is an integer between 1 and M-1, the method is performed by: performing a convolution process on the i-th initial fusion feature map to obtain an i-th weighting factor based on the result of the convolution process; fusing the (i+1)-th initial fusion feature map and the i-th initial fusion feature map based on the i-th weighting factor to obtain an i-th target fusion feature map; extracting key features from the obtained i-th target fusion feature map; performing feature enhancement process on each of the extracted feature maps to obtain a plurality of i-th initial enhanced feature maps; and fusing the plurality of i-th initial enhanced feature maps to obtain the i-th target enhanced feature map; When i is M, the Mth target enhanced feature map is obtained by performing key feature extraction on the Mth initial fusion feature map, performing enhancement processing on each of the extracted feature maps to obtain multiple Mth initial enhanced feature maps, and then performing a fusion processing on the multiple Mth initial enhanced feature maps to obtain the Mth target enhanced feature map.

[0096] For specific functions and exemplary descriptions of each module and sub-module of the apparatus according to the embodiments of the present disclosure, please refer to the relevant descriptions of the corresponding steps in the above-mentioned method embodiments, and they will not be repeated here.

[0097] In the technical solution of the present disclosure, the acquisition, storage, and application of users' personal information comply with the provisions of relevant laws and regulations and do not violate public order and morals.

[0098] FIG. 10 is a structural block diagram of an electronic device according to an embodiment of the present disclosure. As shown in FIG. 10, the electronic device includes a memory 1010 and a processor 1020, and the memory 1010 stores a computer program executable by the processor 1020. The number of memories 1010 and processors 1020 may be one or more. The memory 1010 may store one or more computer programs, which, when executed by the electronic device, cause the electronic device to perform the method provided by the above method embodiments. The electronic device may further include: a communication interface 1030 for communicating with external devices and performing data interaction and transmission;

[0099] When the memory 1010, the processor 1020, and the communication interface 1030 are implemented independently, the memory 1010, the processor 1020, and the communication interface 1030 are connected to each other via a bus to enable communication between them. The bus may be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, an Extended Industry Standard Architecture (EISA) bus, or the like. The bus may be classified into an address bus, a data bus, a control bus, and the like. For ease of explanation, only one bold line is shown in FIG. 10, but this does not represent only one bus or one type of bus.

[0100] Optionally, in a specific implementation, when the memory 1010, the processor 1020, and the communication interface 1030 are integrated on one chip, the memory 1010, the processor 1020, and the communication interface 1030 can communicate with each other via an internal interface.

[0101] It should be understood that the processor may be a Central Processing Unit (CPU), or may be other general-purpose processors, Digital Signal Processing (DSP), Application Specific Integrated Circuits (ASIC), Field Programmable Gate Arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware assemblies, etc. The general-purpose processor may be a microprocessor or any conventional processor, etc. The processor may be a processor supporting the Advanced RISC Machines (ARM) architecture.

[0102] Additionally, the memory may optionally include read-only memory and random access memory, or may further include non-volatile random access memory. The memory may be either volatile or non-volatile memory, or may include both volatile and non-volatile memory. Here, non-volatile memory may include read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), or flash memory. Volatile memory may include random access memory (RAM), which acts as an external cache. By way of example and not limitation, many forms of RAM are available. For example, static random access memory (Static RAM, SRAM), dynamic random access memory (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDR SDRAM), enhanced SDRAM (ESDRAM), synchlink DRAM (SLDRAM), and direct RAMBUS RAM (DR RAM).

[0103] The above-described embodiments may be implemented, in whole or in part, in software, hardware, firmware, or any combination thereof. When implemented in software, they may be implemented, in whole or in part, in the form of a computer program product. A computer program product includes one or more computer instructions. When the computer instructions are loaded and executed on a computer, a process or function according to an embodiment of the present disclosure is generated, in whole or in part. The computer may be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions may be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions may be transmitted from one website site, computer, server, or data center to another website site, computer, server, or data center via wire (e.g., coaxial cable, optical fiber, Digital Subscriber Line (DSL)) or wireless (e.g., infrared, Bluetooth, microwave, etc.). A computer-readable storage medium may be any available medium that can be accessed by a computer, or a data storage device including a server, a data center, etc. integrated with one or more available media. The available medium may be a magnetic medium (e.g., a floppy disk, a hard disk, a magnetic tape), an optical medium (e.g., a Digital Versatile Disc (DVD)), or a semiconductor medium (e.g., a Solid State Disk (SSD)). Note that the computer-readable storage medium referred to in this disclosure may be a non-volatile storage medium, in other words, a non-transitory storage medium.

[0104] Those skilled in the art can understand that all or part of the steps for realizing the above embodiments may be implemented by hardware, or may be implemented by instructing relevant hardware by a program, and the program may be stored in a computer-readable storage medium, and the storage medium may be a read-only memory, a magnetic disk, an optical disk, etc.

[0105] In describing embodiments of the present disclosure, the references "one embodiment," "some embodiments," "examples," "specific examples," or "some examples" mean that the specific features, structures, materials, or characteristics described in connection with the embodiment or example are included in at least one embodiment or example of the present disclosure. Furthermore, the described specific features, structures, materials, or characteristics may be combined in any suitable manner in any one or more embodiments or examples. Furthermore, a person skilled in the art may combine different embodiments or examples and features of different embodiments or examples described in the present disclosure to the extent that they are not inconsistent with each other.

[0106] In the description of the embodiments of the present disclosure, unless otherwise specified, " / " means "or," for example, A / B can mean either A or B. "And / or" in the present disclosure is merely to describe the related relationship of related objects, and indicates that three types of relationships may exist, for example, A and / or B can indicate the following three types of situations: A exists alone, A and B exist simultaneously, and B exists alone.

[0107] In describing the embodiments of the present disclosure, the terms "first" and "second" are used for descriptive purposes only and should not be interpreted as indicating or implying relative importance, nor should they be interpreted as implying the number of technical features shown. Thus, features defined as "first" and "second" may explicitly or implicitly include one or more of such features. In describing the embodiments of the present disclosure, "plurality" means two or more, unless otherwise specified.

[0108] The above are merely illustrative examples of the present disclosure, and do not limit the present disclosure. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principles of the present disclosure should be included in the scope of protection of the present disclosure.

Claims

1. A method for processing wound yarn package data, comprising: When it is determined that the transported wound yarn package has entered a detection area, a defect detection is performed on the wound yarn package located within the detection area to obtain a target detection result for the wound yarn package, and the target detection result is used to indicate the degree of the defect in the wound yarn package; and obtaining a target grade for the wound yarn package based on the target detection result of the wound yarn package and a predetermined grade for the wound yarn package when it is determined that the target detection result satisfies a predetermined defect requirement. A method for processing wound yarn package data.

2. performing defect detection on a wound yarn package located within the detection area and obtaining a target detection result for the wound yarn package, acquiring N images of the wound yarn package, each of the N images including at least a portion of the wound yarn package, where N is 3 or greater; inputting the N images into a target detection model to obtain a first detection result corresponding to each image in the N images, the target detection model being used to recognize whether a defect exists in a local area of the wound yarn package and obtain a first detection result, the first detection result including at least one of a defect number, a defect location, and a defect type; obtaining a target detection result of the wound yarn package based on a first detection result corresponding to each image among the N images; The method for processing wound yarn package data according to claim 1.

3. Obtaining a target detection result of the wound yarn package based on a first detection result corresponding to each image among the N images includes: obtaining information about defects in the wound yarn package and determining a target detection result indicative of a degree of defect in the wound yarn package based on the information about the defects in the wound yarn package; calculating a total number of defects in the wound yarn package by calculating the number of defects in each of the N first detection results; obtaining all defect positions in the wound yarn package based on defect positions included in each of the N first detection results; and obtaining a target defect type for the wound yarn package based on a defect type included in each of the N first detection results. The method for processing wound yarn package data according to claim 2.

4. When it is determined that the target detection result satisfies a preset defect requirement, obtaining a target grade of the wound yarn package based on the target detection result of the wound yarn package and a preset grade of the wound yarn package; the total number of defects in the wound yarn package in the target detection result is greater than a preset threshold value; and all defect positions of the wound yarn package in the target detection result are within a predetermined defect position range; and when it is determined that at least one of the target defect type of the wound yarn package in the target detection result is within a preset defect type range, downgrading the preset grade of the wound yarn package to obtain the target grade of the detected wound yarn package. The method for processing wound yarn package data according to claim 3.

5. the target detection model includes at least a first network layer, a second network layer, and a third network layer; The first network layer is used to perform feature processing on an input image to obtain a low-level feature map, and the low-level feature map is used to represent the feature map extracted after removing background noise in the image; The second network layer is used to perform feature enhancement processing on key feature information in at least the low-level feature map to obtain a high-level feature map; the third network layer is used to perform defect recognition based on the high-level feature map to obtain a first detection result; The method for processing wound yarn package data according to claim 2.

6. the first network layer includes at least a first subnetwork layer, a second subnetwork layer, a third subnetwork layer, and a fourth subnetwork layer; The first sub-network layer is used to extract local features from the input image to obtain a low-frequency feature map; the second sub-network layer is used to obtain a target weighting coefficient for the feature map of the input image; the third sub-network layer is used to extract global features from the input image to obtain a global feature map; and the fourth sub-network layer is used to perform noise removal by merging the low-frequency feature map and the global feature map based on the target weighting coefficient to obtain a low-level feature map. The method for processing wound yarn package data according to claim 5.

7. the second network layer includes at least a fifth subnetwork layer, a sixth subnetwork layer, and a seventh subnetwork layer; The fifth sub-network layer is used to perform feature extraction on the low-level feature maps, and to fuse the extracted feature maps to obtain M initial fused feature maps, where M is an integer greater than or equal to 2; The sixth sub-network layer is used for extracting key features from each of the M initial fusion feature maps, and for enhancing the key feature information extracted from each of the M initial fusion feature maps to obtain M target enhanced feature maps; The seventh sub-network layer is used to fuse the obtained M target-enhanced feature maps to obtain a high-level feature map. The method for processing wound yarn package data according to claim 6.

8. The i-th target-enhanced feature map among the M target-enhanced feature maps is When i is an integer between 1 and M-1, the i-th initial fusion feature map is subjected to a convolution process to obtain an i-th weighting factor based on the result of the convolution process, the i+1-th initial fusion feature map and the i-th initial fusion feature map are subjected to a fusion process based on the i-th weighting factor to obtain an i-th target fusion feature map, key features are extracted from the obtained i-th target fusion feature map, feature enhancement processes are performed on each of the extracted feature maps to obtain a plurality of i-th initial enhanced feature maps, and the plurality of i-th initial enhanced feature maps are subjected to a fusion process to obtain the i-th target enhanced feature map, When i is M, the Mth target enhanced feature map is obtained by performing key feature extraction on the Mth initial fusion feature map, performing enhancement processing on each of the extracted feature maps to obtain a plurality of Mth initial enhanced feature maps, and performing a fusion processing on the plurality of Mth initial enhanced feature maps to obtain the Mth target enhanced feature map. The method for processing wound yarn package data according to claim 7.

9. A processing device for wound yarn package data, a defect detection unit for detecting defects in the wound yarn package located within the detection area when it is determined that the wound yarn package being conveyed has entered the detection area, and obtaining a target detection result for the wound yarn package, the target detection result being used to indicate the degree of a defect in the wound yarn package; a grade determination unit for determining a target grade of the wound yarn package based on the target detection result of the wound yarn package and a preset grade of the wound yarn package when it is determined that the target detection result satisfies a preset defect requirement. A processing device for wound yarn package data.

10. at least one processor; a memory communicatively coupled to the at least one processor; The memory stores instructions executable by the at least one processor, the instructions, when executed by the at least one processor, causing the at least one processor to perform the method of any one of claims 1 to 8. Electronic devices.

11. A non-transitory computer-readable storage medium having stored thereon instructions for causing a computer to perform the method of any one of claims 1 to 8.

12. A program for implementing the method of any one of claims 1 to 8 when executed by a processor in a computer.

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