Concrete defect detection method and device, electronic equipment and storage medium

Abstract

The invention discloses a concrete defect detection method and device, electronic equipment and a storage medium, the concrete defect detection method comprises the following steps: in response to a received detection instruction, obtaining a current concrete image of concrete, the detection instruction being used for indicating defect detection on the concrete; the current concrete image is input to a target defect detection model, a corresponding defect detection result is obtained, the target defect detection model is obtained based on a single-step multi-frame target detection model and a texture enhancement model, and the texture enhancement model is used for enhancing and extracting texture features of the current concrete image. According to the method, the target defect detection model constructed based on the single-step multi-frame target detection model and the texture enhancement model is used for detecting the defects of the concrete, the detection time is short, and the detection efficiency and the detection accuracy of detecting the defects of the concrete are improved.

Description

Technical Field

[0001] The present application belongs to the technical field of concrete detection, and in particular relates to a concrete defect detection method, a detection device, an electronic device, and a storage medium. Background Art

[0002] Concrete boasts excellent integrity, moldability, durability, and fire resistance, making it widely used in the construction of infrastructure such as buildings, bridges, tunnels, and dams. However, due to factors such as material aging, temperature fluctuations, and long-term loads, concrete is prone to defects during construction and operation, such as cracks and breakage. These defects reduce the concrete's bearing capacity and increase safety risks. Therefore, defect detection in concrete is particularly important.

[0003] At present, the defect detection of concrete mainly relies on manual inspection and observation of concrete, which is time-consuming and may result in missed detection or misjudgment of defects. Summary of the Invention

[0004] In view of this, embodiments of the present application provide a concrete defect detection method, a detection device, an electronic device, and a storage medium to overcome or at least partially solve the above problems of the prior art.

[0005] In a first aspect, an embodiment of the present application provides a concrete defect detection method, comprising: obtaining a current concrete image of concrete in response to a received detection instruction, wherein the detection instruction is used to instruct defect detection on the concrete; inputting the current concrete image into a target defect detection model to obtain corresponding defect detection results, wherein the target defect detection model is obtained based on a single-step multi-frame target detection model and a texture enhancement model, and the texture enhancement model is used to enhance and extract texture features of the current concrete image.

[0006] In a second aspect, embodiments of the present application provide a concrete defect detection device, comprising a first acquisition module and a first input module. The first acquisition module is configured to acquire a current concrete image in response to a received detection instruction, wherein the detection instruction is configured to instruct the concrete to undergo defect detection. The first input module is configured to input the current concrete image into a target defect detection model to obtain a corresponding defect detection result. The target defect detection model is derived from a single-step multi-frame target detection model and a texture enhancement model, wherein the texture enhancement model is configured to enhance and extract texture features of the current concrete image.

[0007] In a third aspect, an embodiment of the present application provides an electronic device comprising a memory; one or more processors coupled to the memory; and one or more applications, wherein the one or more applications are stored in the memory and configured to be executed by the one or more processors, and the one or more applications are configured to execute the concrete defect detection method provided in the first aspect above.

[0008] In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium, in which a program code is stored. The program code can be called by a processor to execute the concrete defect detection method provided in the first aspect above.

[0009] In a fifth aspect, an embodiment of the present application provides a computer program product. When the computer program product is run on a computer device, the computer device executes the concrete defect detection method provided in the first aspect above.

[0010] The solution provided in the present application obtains a current concrete image of concrete in response to a received detection instruction, the detection instruction is used to instruct defect detection on the concrete, and the current concrete image is input into a target defect detection model to obtain a corresponding defect detection result. The target defect detection model is obtained based on a single-step multi-frame target detection model and a texture enhancement model. The texture enhancement model is used to enhance and extract texture features of the current concrete image, thereby realizing defect detection of concrete based on the target defect detection model constructed based on the single-step multi-frame target detection model and the texture enhancement model. The detection time is short, thereby improving the detection efficiency and accuracy of defect detection on concrete.

[0011] In addition, a target defect detection model is constructed based on the single-step multi-frame target detection model and the texture enhancement model, which is conducive to improving the learning ability of the target defect detection model to learn the texture features of the current concrete image, making concrete defects easier to detect, and is conducive to further improving the detection accuracy of concrete defects. BRIEF DESCRIPTION OF THE DRAWINGS

[0012] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the following briefly introduces the drawings required for use in the embodiments or descriptions of the prior art. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0013] Figure 1 A schematic diagram of a scenario of a concrete defect detection system provided in an embodiment of the present application is shown.

[0014] Figure 2A flow chart of a concrete defect detection method provided in an embodiment of the present application is shown.

[0015] Figure 3 Another flow chart of the concrete defect detection method provided in an embodiment of the present application is shown.

[0016] Figure 4 A structural schematic diagram of a texture enhancement model in the concrete defect detection method provided in an embodiment of the present application is shown.

[0017] Figure 5 A structural schematic diagram of an initial defect detection model in the concrete defect detection method provided in an embodiment of the present application is shown.

[0018] Figure 6 Another flow chart of the concrete defect detection method provided in the embodiment of the present application is shown.

[0019] Figure 7 Another flow chart of the concrete defect detection method provided in an embodiment of the present application is shown.

[0020] Figure 8 A structural block diagram of a concrete defect detection device provided in an embodiment of the present application is shown.

[0021] Figure 9 A functional block diagram of an electronic device provided in an embodiment of the present application is shown.

[0022] Figure 10 A computer-readable storage medium provided in an embodiment of the present application is shown for storing or carrying program codes for implementing the concrete defect detection method provided in an embodiment of the present application.

[0023] Figure 11 A computer program product provided in an embodiment of the present application is shown, which is used to store or carry program codes for implementing the concrete defect detection method provided in an embodiment of the present application. DETAILED DESCRIPTION

[0024] In order to make the purpose, features, and advantages of the invention of this application more obvious and easy to understand, the technical solutions in the embodiments of this application will be clearly and completely described below in conjunction with the drawings in the embodiments of this application. Obviously, the embodiments described below are only part of the embodiments of this application, not all of the embodiments. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of this application.

[0025] It will be understood that when used in this specification and the appended claims, the term "comprising" indicates the presence of described features, integers, steps, operations, elements and / or components, but does not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components and / or groups thereof.

[0026] It should also be understood that the terms used in this specification are for the purpose of describing specific embodiments only and are not intended to limit the present application. As used in this specification and the appended claims, the singular forms "a," "an," and "the" are intended to include the plural forms unless the context clearly indicates otherwise.

[0027] It should be further understood that the term "and / or" used in this specification and the appended claims refers to and includes any and all possible combinations of one or more of the associated listed items.

[0028] In addition, in the description of the present application, the terms "first", "second", "third", etc. are only used to distinguish the description and cannot be understood as indicating or implying relative importance.

[0029] Concrete boasts excellent integrity, moldability, durability, and fire resistance, making it widely used in the construction of infrastructure such as buildings, bridges, tunnels, and dams. However, due to factors such as material aging, temperature fluctuations, and long-term loads, concrete is prone to defects during construction and operation, such as cracks and breakage. These defects reduce the concrete's bearing capacity and increase safety risks. Therefore, defect detection in concrete is particularly important.

[0030] At present, the defect detection of concrete mainly relies on manual inspection and observation of concrete, which is time-consuming and may result in missed detection or misjudgment of defects.

[0031] In response to the above problems, the concrete defect detection method, detection device, electronic device and storage medium provided in the embodiments of the present application obtain the current concrete image of the concrete in response to the received detection instruction, the detection instruction is used to instruct defect detection on the concrete, and the current concrete image is input into the target defect detection model to obtain the corresponding defect detection result. The target defect detection model is obtained based on the single-step multi-frame target detection model and the texture enhancement model. The texture enhancement model is used to enhance and extract the texture features of the current concrete image, and realizes defect detection of concrete by the target defect detection model constructed based on the single-step multi-frame target detection model and the texture enhancement model. The detection time is short, which improves the detection efficiency and detection accuracy of concrete defects.

[0032] In addition, a target defect detection model is constructed based on the single-step multi-frame target detection model and the texture enhancement model, which is conducive to improving the learning ability of the target defect detection model to learn the texture features of the current concrete image, making concrete defects easier to detect, and is conducive to further improving the detection accuracy of concrete defects.

[0033] The technical solutions in the embodiments of the present application will be described clearly and completely below in conjunction with the drawings in the embodiments of the present application.

[0034] See also Figure 1 , which shows a schematic diagram of an application scenario of the concrete defect detection system provided by an embodiment of the present application, which may include concrete 100, a camera 200 and a processing device 300. The camera 200 can be installed relative to the concrete 100, and the camera 200 can be connected to the processing device 300 through a network and exchange data with the processing device 300 through the network.

[0035] The concrete 100 may be any one of cement concrete, silicate concrete, gypsum concrete, water glass concrete, asphalt concrete or polymer concrete. The type of concrete 100 is not limited here and can be set according to actual needs.

[0036] The camera 200 can be used to capture images of the concrete 100 to obtain a concrete image, and send the concrete image to the processing device 300 via a network.

[0037] The camera 200 can be any one of a wide-angle camera, a macro camera, an ultra-wide-angle camera or a panoramic camera, etc. The type of the camera 200 is not limited here and can be set according to actual needs.

[0038] The processing device 300 can be used to receive the concrete image sent by the camera 200 and perform defect detection on the concrete 100 based on the concrete image. The processing device 300 can be any one of a server or a terminal device, etc., which is not limited here and can be specifically configured according to actual needs.

[0039] The server can be an independent physical server, a server cluster or distributed system composed of multiple physical servers, or a cloud server that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communications, middleware services, domain name services, security services, content delivery networks (CDNs), big data, and artificial intelligence platforms.

[0040] The terminal device may be a mobile terminal device (eg, a vehicle-mounted terminal, a PDA (Personal Digital Assistant), a tablet PC (Tablet Personal Computer, Tablet PC), a laptop computer, etc.), or a fixed terminal device (a desktop computer, a smart panel, etc.).

[0041] The network may be any one of a ZigBee network, a Bluetooth (BT) network, a Wireless Fidelity (Wi-Fi) network, a Thread network, a Long Range Radio (LoRa) network, a Low-Power Wide-Area Network (LPWAN), an infrared network, a Narrow Band Internet of Things (NB-IoT), a Controller Area Network (CAN), a Digital Living Network Alliance (DLNA) network, a Wide Area Network (WAN), a Local Area Network (LAN), a Metropolitan Area Network (MAN), or a Wireless Personal Area Network (WPAN), etc., without limitation here.

[0042] See also Figure 2 , which shows a flow chart of a concrete defect detection method provided by an embodiment of the present application. In a specific embodiment, the concrete defect detection method can be applied to Figure 1 The processing device 300 in the concrete defect detection system shown in FIG. 3 is taken as an example below. Figure 2 The process shown in FIG. 1 is described in detail. The concrete defect detection method may include the following steps S110 to S120.

[0043] Step S110: In response to the received detection instruction, a current concrete image of the concrete is acquired.

[0044] In an embodiment of the present application, when the inspector needs to perform defect detection on concrete, he or she can send a detection instruction to the processing device. The processing device receives and responds to the detection instruction, sends a first acquisition instruction to the camera via the network, and the camera receives and responds to the first acquisition instruction, performs image capture on the concrete, obtains the current concrete image, and sends the current concrete image to the processing device via the network. The processing device receives the current concrete image returned by the camera.

[0045] The detection instruction can be used to instruct the processing equipment to perform defect detection on the concrete.

[0046] In some embodiments, the processing device may detect the inspector's operation. When it is determined based on the detected inspector's operation that the inspector has input an instruction to perform defect detection on concrete, the processing device receives the instruction to perform defect detection on concrete.

[0047] For example, when the inspector needs to perform defect detection on concrete, he or she can perform touch operations on the operation panel of the processing equipment. The processing equipment responds to the inspector's touch operation, generates a corresponding touch signal, and analyzes the touch signal. When it is determined that the touch signal is a preset detection signal for characterizing defect detection on concrete, it is determined that a detection instruction for defect detection on concrete has been received.

[0048] In some embodiments, the processing device may be provided with a voice recognition module. When the inspector needs to perform defect detection on concrete, the inspector may send voice information within the voice collection range of the voice recognition module. The voice recognition module collects the voice information sent by the inspector and performs voice recognition on the collected voice information. Based on the recognition result of the voice recognition, it is determined that the recognition result contains keywords for indicating defect detection on concrete, such as "concrete defect detection", or "concrete" and "defect detection", etc., and it is determined that a detection instruction for defect detection on concrete has been received.

[0049] As an example, the voice message sent by the inspector is: perform defect detection on concrete, and the recognition result of the voice recognition contains the keywords "concrete" and "defect detection", then it is determined that the detection instruction for performing defect detection on concrete has been received.

[0050] In some embodiments, the concrete defect detection system may further include a client associated with the inspector, wherein the client is connected to the processing device via a network and exchanges data with the processing device via the network.

[0051] When inspectors need to perform defect detection on concrete, they can send detection instructions to the client. The client receives and responds to the detection instructions and forwards the detection instructions to the processing device through the network. The processing device receives the detection instructions forwarded by the client.

[0052] Among them, the client can be any one of a mobile client (for example, a mobile phone client, a PDA client, a Tablet PC client, a laptop client, a smart watch client, a smart bracelet client or a wearable client, etc.) or a fixed client (for example, a desktop computer client, a smart panel client, etc.). The type of client is not limited here and can be set according to actual needs.

[0053] Step S120: input the current concrete image into the target defect detection model to obtain the corresponding defect detection result.

[0054] In an embodiment of the present application, after the processing device obtains the current concrete image of the concrete in response to the received detection instruction, the current concrete image can be input into the target defect detection model. The target defect detection model receives and responds to the current concrete image, performs defect detection on the concrete according to the current concrete image, obtains the corresponding defect detection result, and outputs the defect detection result to the processing device. The processing device receives the defect detection result output by the target defect detection model. The target defect detection model can be obtained based on the single-step multi-box target detection (Single Shot Multi-Box Detector, SSD) model and the texture enhancement model. The texture enhancement model can be used to enhance and extract the texture features of the current concrete image, thereby realizing defect detection of concrete by the target defect detection model constructed based on the single-step multi-box target detection model and the texture enhancement model. The detection time is short, which improves the detection efficiency and detection accuracy of concrete defects.

[0055] In addition, a target defect detection model is constructed based on the single-step multi-frame target detection model and the texture enhancement model, which is conducive to improving the learning ability of the target defect detection model to learn the texture features of the current concrete image, making concrete defects easier to detect, and is conducive to further improving the detection accuracy of concrete defects.

[0056] The defect detection result may include a first detection result used to indicate that the current concrete image contains the current defect information, and a second detection result used to indicate that the current concrete image does not contain the current defect information.

[0057] The current defect information may include defect type (eg, crack, breakage, exposed steel bar, etc.), defect location, and defect size, etc. The type of the current defect information is not limited here and can be set according to actual needs.

[0058] The solution provided in the present application obtains a current concrete image of concrete in response to a received detection instruction, the detection instruction is used to instruct defect detection on the concrete, and the current concrete image is input into a target defect detection model to obtain a corresponding defect detection result. The target defect detection model is obtained based on a single-step multi-frame target detection model and a texture enhancement model. The texture enhancement model is used to enhance and extract texture features of the current concrete image, thereby realizing defect detection of concrete based on the target defect detection model constructed based on the single-step multi-frame target detection model and the texture enhancement model. The detection time is short, thereby improving the detection efficiency and accuracy of defect detection on concrete.

[0059] In addition, a target defect detection model is constructed based on the single-step multi-frame target detection model and the texture enhancement model, which is conducive to improving the learning ability of the target defect detection model to learn the texture features of the current concrete image, making concrete defects easier to detect, and is conducive to further improving the detection accuracy of concrete defects.

[0060] See also Figure 3 , which shows a flow chart of a concrete defect detection method provided by another embodiment of the present application. In a specific embodiment, the concrete defect detection method can be applied to Figure 1 The processing device 300 in the concrete defect detection system shown in FIG. 3 is taken as an example below. Figure 3 The process shown in FIG. 1 is described in detail. The concrete defect detection method may include the following steps S210 to S250.

[0061] Step S210: constructing a texture enhancement model.

[0062] In this embodiment, the processing device can fuse the central difference convolution (CDC) module, the horizontal differential convolution (HDC) module, the vertical differential convolution (VDC) module, the angular differential convolution (ADC) module and the channel shuffle (CS) module to obtain a texture enhancement model.

[0063] Among them, the CDC module can be used to calculate the texture feature difference between the central pixel of the current concrete image and the surrounding neighborhood to obtain a first feature map, and the central pixel can be used to represent the pixel point at the center position of the current concrete image; the HDC module can be used to extract the horizontal texture features of the first feature map to obtain a second feature map; the VDC module can be used to extract the vertical texture features of the second feature map to obtain a third feature map; the ADC module can be used to extract texture features at multiple angles of the third feature map to obtain a fourth feature map; the CS module is used to enhance the cross-channel communication of the texture features of the first feature map, the second feature map, the third feature map, and the fourth feature map to obtain a fifth feature map.

[0064] The texture feature can be used to represent the grayscale of the current concrete image, and the texture feature difference can be used to represent the grayscale difference of the current concrete image.

[0065] like Figure 4 As shown, the texture enhancement model can include a CDC module, an HDC module, a VDC module, an ADC module, and a CS module connected in sequence. The CDC module, HDC module, and VDC module can all be connected to the CS module via a shortcut. The shortcut connection enables the texture enhancement model to learn deeper texture features, which is beneficial to improving the texture enhancement model's ability to learn the texture features of the current concrete image.

[0066] Step S220: Fusing the single-step multi-box target detection model and the texture enhancement model to obtain an initial defect detection model.

[0067] In this embodiment, after the processing device constructs the texture enhancement model, it can fuse the Visual Geometry Group (VGG) module of the SSD model, the top-down module in the Feature Pyramid Networks (FPN), and the texture enhancement model to obtain an initial defect detection model. The texture enhancement model can enhance and extract the texture features of the current concrete image, which is conducive to improving the detection accuracy of the initial defect detection model for concrete defects.

[0068] Among them, the SSD model can include a VGG module and an FPN, and the FPN can include a top-down module and a bottom-up module.

[0069] The processing device can splice the texture enhancement model between the VGG module and the top-down module in FPN to obtain the initial defect detection model. Figure 5 As shown, the initial defect detection model may include a VGG module, a texture enhancement model, a top-down module, and a bottom-up module connected in sequence.

[0070] Step S230: training the initial defect detection model to obtain a target defect detection model.

[0071] In this embodiment, after the processing device fuses the SSD model and the texture enhancement model to obtain the initial defect detection model, it can obtain historical concrete defect images and obtain corresponding sample sets based on the historical concrete defect images. The sample sets may include training sets and test sets, and the training sets are input into the initial defect detection model. The initial defect detection model receives and responds to the training set, and is trained according to the training set to obtain a target defect detection model. The initial defect detection model constructed based on the single-step multi-frame target detection model and the texture enhancement model is trained to obtain a target defect detection model. The texture enhancement model can enhance and extract texture features of the current concrete image, which is beneficial to improving the detection accuracy of the target defect detection model for concrete defects.

[0072] The processing device can annotate historical concrete defect images to obtain annotated images, and divide the annotated images according to preset division rules to obtain training sets and test sets.

[0073] When the processing equipment annotates the historical concrete defect image, it mainly annotates the defect type, defect location, defect size, etc. in the historical concrete defect image.

[0074] For example, the processing device may use a marking frame to mark the defect type in the historical concrete defect image, mark the coordinates of the corner points of the marking frame, mark the size of the marking frame, and so on.

[0075] The preset division rule may be an artificial division rule. For example, the preset division rule may be an artificial division rule of training set: test set = 7:3. When the number of historical concrete defect images is 20,000, the training set is 14,000 and the test set is 6,000. The preset division rule may be an artificial division rule of training set: test set = 9:1. When the number of historical concrete defect images is 30,000, the training set is 27,000 and the test set is 3,000. The division method of the preset division rule is not limited here and can be set according to actual needs.

[0076] Regarding the process of the processing device acquiring the historical concrete defect image, in some embodiments, the processing device pre-stores the historical concrete defect image, and the processing device can read the pre-stored historical concrete defect image.

[0077] Regarding the process of the processing device acquiring the historical concrete defect images, in some embodiments, the processing device may generate prompt information and receive the historical concrete defect images uploaded by the inspection personnel according to the prompt information.

[0078] The prompt information can be used to prompt the inspector to upload historical concrete defect images to the processing device. The prompt information can include at least one of text prompt information, sound prompt information, and light prompt information. The type of prompt information is not limited here and can be set according to actual needs.

[0079] Regarding the process of the above-mentioned processing device obtaining historical concrete defect images, in some embodiments, the concrete defect detection system may also include a detection service platform, which pre-stores historical concrete defect images. The detection service platform can be connected to the processing device via a network and interact with the detection service platform through the network.

[0080] The processing device can send a second acquisition instruction to the detection service platform via the network. The detection service platform receives and responds to the second acquisition instruction, sends the historical concrete defect image to the processing device via the network, and the processing device receives the historical concrete defect image returned by the detection service platform.

[0081] Among them, the detection service platform can be an independent physical cloud platform, or a cloud platform cluster or distributed system composed of multiple physical cloud platforms, or any one of the cloud platforms that provide basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communications, middleware services, domain name services, security services, content delivery networks (CDN), big data or artificial intelligence platforms, etc., and is not limited here.

[0082] In some embodiments, after the processing device fuses the SSD model and the texture enhancement model to obtain the initial defect detection model, it can obtain historical concrete defect images and obtain corresponding sample sets based on the historical concrete defect images. The sample sets may include training sets and test sets, and the training set is subjected to data enhancement processing to obtain an enhanced training set, and the enhanced training set is input into the initial defect detection model. The initial defect detection model receives and responds to the enhanced training set, and is trained according to the enhanced training set to obtain a target defect detection model. This realizes the training of the initial defect detection model according to the enhanced training set, avoids the low robustness and generalization ability of the target defect detection model obtained by training the initial defect detection model with a small number of training sets due to the small number of historical concrete defect images, and improves the robustness and generalization ability of the target defect detection model.

[0083] Among them, data enhancement processing can include at least any one of brightness enhancement processing, grayscale enhancement processing, contrast enhancement processing, affine transformation enhancement processing and transparency enhancement processing. The type of data enhancement processing is not limited here and can be set according to actual needs.

[0084] Step S240: In response to the received detection instruction, a current concrete image of the concrete is acquired.

[0085] Step S250: input the current concrete image into the target defect detection model to obtain the corresponding defect detection result.

[0086] In this embodiment, step S240 and step S250 may refer to the contents of the corresponding steps in the aforementioned embodiment, and will not be repeated here.

[0087] The solution provided in this embodiment constructs a texture enhancement model and integrates the single-step multi-frame target detection model and the texture enhancement model to obtain an initial defect detection model, and trains the initial defect detection model to obtain a target defect detection model. In response to the received detection instruction, the current concrete image of the concrete is obtained, and the current concrete image is input into the target defect detection model to obtain the corresponding defect detection result. This realizes defect detection of concrete based on the target defect detection model constructed by the single-step multi-frame target detection model and the texture enhancement model, shortens the detection time, and improves the detection efficiency and accuracy of defect detection of concrete.

[0088] And based on the training of the initial defect detection model constructed by the single-step multi-frame target detection model and the texture enhancement model, a target defect detection model is obtained. The texture enhancement model can enhance and extract the texture features of the current concrete image, which is beneficial to improving the detection accuracy of the target defect detection model for concrete defects.

[0089] See also Figure 6 , which shows a flow chart of a concrete defect detection method provided by another embodiment of the present application. In a specific embodiment, the concrete defect detection method can be applied to Figure 1 The processing device 300 in the concrete defect detection system shown in FIG. 3 is taken as an example below. Figure 6 The process shown in FIG. 1 is described in detail. The concrete defect detection method may include the following steps S310 to S340.

[0090] Step S310: In response to the received detection instruction, a current concrete image of the concrete is acquired.

[0091] Step S320: input the current concrete image into the target defect detection model to obtain the corresponding defect detection result.

[0092] In this embodiment, step S310 and step S320 may refer to the contents of the corresponding steps in the aforementioned embodiment, and will not be repeated here.

[0093] Step S330: When the defect detection result is a first detection result used to characterize that the current concrete image contains current defect information, the first detection result is input into the remaining life prediction model to obtain the remaining life of the concrete.

[0094] In this embodiment, the processing device inputs the current concrete image into the target defect detection model, and after obtaining the corresponding defect detection result, when the defect detection result is a first detection result used to characterize that the current concrete image contains current defect information, the first detection result is input into the remaining life prediction model, the remaining life prediction model receives and responds to the first detection result, obtains the remaining life of the concrete, and outputs the remaining life of the concrete to the processing device, and the processing device receives the remaining life of the concrete output by the remaining life prediction model.

[0095] Among them, the remaining life prediction model can be obtained by training the deep learning neural network model based on historical defect information.

[0096] The historical defect information may include defect type, defect location, defect size, etc. The type of historical defect information is not limited here and can be set according to actual needs.

[0097] The deep learning neural network model can be a convolutional neural network (CNN) model, a deep belief network (DBN) model, a stacked autoencoder network (SAE) model, a recurrent neural network (RNN) model, a deep neural network (DNN) model, a long short-term memory (LSTM) network model or a gated recurring unit (GRU) model, etc. The type of deep learning neural network model is not limited here and can be set according to actual needs.

[0098] Step S340: When the remaining life of the concrete is less than or equal to the remaining life threshold, a corresponding warning message is generated.

[0099] In this embodiment, when the remaining life of concrete is less than or equal to the remaining life threshold, a corresponding warning message can be generated to alert the inspection personnel so that the inspection personnel can handle the concrete in time. This can suppress the problem of increased safety risk of concrete when the remaining life of concrete is low and the minimum remaining life, which is a safety risk, occurs, thereby reducing the safety risk of concrete.

[0100] Among them, the remaining life threshold can be used to characterize the minimum remaining life of concrete at which safety risks occur. The remaining life threshold can be a life value pre-set by the inspection personnel, or it can be a life value automatically generated by the processing equipment based on the process of multiple defect inspections on concrete. It is not limited here and can be set according to actual needs.

[0101] The warning information may be at least any one of a sound warning information, a text warning information or a light warning information, etc. The type of warning information is not limited here and can be set according to actual needs.

[0102] The solution provided in this embodiment obtains the current concrete image of the concrete in response to the received detection instruction, and inputs the current concrete image into the target defect detection model to obtain the corresponding defect detection result. When the defect detection result is the first detection result used to characterize that the current concrete image contains the current defect information, the first detection result is input into the remaining life prediction model to obtain the remaining life of the concrete. When the remaining life of the concrete is less than or equal to the remaining life threshold, a corresponding warning message is generated. This realizes defect detection of concrete based on the target defect detection model constructed based on the single-step multi-frame target detection model and the texture enhancement model. The detection time is short, and the detection efficiency and accuracy of defect detection of concrete are improved.

[0103] And when the remaining life of concrete is less than or equal to the remaining life threshold, a corresponding warning message can be generated to alert the inspection personnel so that the inspection personnel can deal with the concrete in time. This can suppress the problem of increased safety risk of concrete when the remaining life of concrete is low and the minimum remaining life of concrete presents a safety risk, thereby reducing the safety risk of concrete.

[0104] See also Figure 7 , which shows a flow chart of a concrete defect detection method provided by another embodiment of the present application. In a specific embodiment, the concrete defect detection method can be applied to Figure 1 The processing device 300 in the concrete defect detection system shown in FIG. 3 is taken as an example below. Figure 7 The process shown in FIG. 1 is described in detail. The concrete defect detection method may include the following steps S410 to S440.

[0105] Step S410: In response to the received detection instruction, a current concrete image of the concrete is acquired.

[0106] Step S420: input the current concrete image into the target defect detection model to obtain the corresponding defect detection result.

[0107] In this embodiment, step S410 and step S420 may refer to the contents of the corresponding steps in the aforementioned embodiment, and will not be repeated here.

[0108] Step S430: When the defect detection result is a first detection result used to indicate that the current concrete image contains current defect information, a defect treatment solution corresponding to the first detection result is obtained.

[0109] In this embodiment, the processing device inputs the current concrete image into the target defect detection model, and after obtaining the corresponding defect detection result, when the defect detection result is a first detection result used to characterize that the current concrete image contains current defect information, a defect processing solution corresponding to the first detection result can be obtained.

[0110] In some implementations, the processing device may pre-store a solution table, which may be used to represent the correspondence between defect information and processing solutions.

[0111] The processing device inputs the current concrete image into the target defect detection model and obtains the corresponding defect detection result. When the defect detection result is a first detection result used to characterize that the current concrete image contains current defect information, the solution table can be searched according to the first detection result to obtain a defect processing solution.

[0112] For example, the defect information may include first sub-defect information (defect type A1, defect position B1 and defect size C1), second sub-defect information (defect type A1, defect position B1 and defect size C2), third sub-defect information (defect type A1, defect position B2 and defect size C1), fourth sub-defect information (defect type A1, defect position B2 and defect size C2), fifth sub-defect information (defect type A2, defect position B1 and defect size C1), sixth sub-defect information (defect type A2, defect position B1 and defect size C2), seventh sub-defect information (defect type A2, defect position B2 and defect size C1) and eighth sub-defect information (defect type A2, defect position B2 and defect size C2).

[0113] The treatment schemes may include a treatment scheme D1 , a treatment scheme D2 , a treatment scheme D3 , a treatment scheme D4 , a treatment scheme D5 , a treatment scheme D6 , a treatment scheme D7 , and a treatment scheme D8 .

[0114] The correspondence between defect information and processing solutions can be shown in Table 1, ie, a solution table. The defect processing solution can be obtained based on the correspondence.

[0115] Table 1

[0116] Defect Information Solution First sub-defect information D2 Second sub-defect information D5 Third sub-defect information D1 Fourth sub-defect information D3 Fifth child defect information D4 Sixth child defect information D8 Seventh Child Defect Information D6 Eighth sub-defect information D7

[0117] It should be noted that the correspondence between defect information and processing solutions is not limited to that shown in Table 1, and can be set according to actual needs.

[0118] In some embodiments, the concrete defect detection system may further include a detection service platform. The detection service platform may pre-store a solution table, which may be used to correspond between defect information and treatment solutions.

[0119] The processing device inputs the current concrete image into the target defect detection model, and after obtaining the corresponding defect detection result, when the defect detection result is a first detection result used to characterize that the current concrete image contains current defect information, the first detection result can be sent to the detection service platform via the network. The detection service platform receives and responds to the first detection result, searches the solution table, obtains a defect treatment solution, and sends the defect treatment solution to the processing device via the network. The processing device receives the defect treatment solution returned by the detection service platform.

[0120] Step S440: Send the defect handling solution to the client associated with the inspector.

[0121] In this embodiment, the concrete defect detection system may further include a client associated with the inspector. When the defect detection result is a first detection result used to characterize that the current concrete image contains current defect information, after the processing device obtains the defect treatment plan corresponding to the first detection result, the defect treatment plan can be sent to the client via the network so that the inspector can promptly process the concrete according to the defect treatment plan, thereby improving the user experience of the inspector during the process of defect detection of concrete.

[0122] The solution provided in this embodiment obtains a current concrete image of concrete in response to a received detection instruction, inputs the current concrete image into a target defect detection model, obtains a corresponding defect detection result, and when the defect detection result is a first detection result used to characterize that the current concrete image contains current defect information, obtains a defect treatment plan corresponding to the first detection result, and sends the defect treatment plan to a client associated with the inspection personnel, thereby realizing defect detection of concrete based on a target defect detection model constructed based on a single-step multi-frame target detection model and a texture enhancement model, shortening the detection time and improving the detection efficiency and accuracy of defect detection of concrete.

[0123] The defect treatment plan corresponding to the first detection result is sent to the client associated with the inspector, so that the inspector can promptly process the concrete according to the defect treatment plan, thereby improving the user experience of the inspector during the process of defect detection of concrete.

[0124] See also Figure 8, which shows a concrete defect detection device 500 provided by an embodiment of the present application. The concrete defect detection device 500 can be applied to Figure 1 The processing device 300 in the concrete defect detection system shown in FIG. 3 is taken as an example below. Figure 8 The concrete defect detection device 500 shown in FIG. 5 is described in detail. The concrete defect detection device 500 may include a first acquisition module 510 and a first input module 520 .

[0125] The first acquisition module 510 can be used to obtain the current concrete image of the concrete in response to the received detection instruction, and the detection instruction can be used to instruct defect detection of the concrete; the first input module 520 can be used to input the current concrete image into the target defect detection model to obtain the corresponding defect detection result. The target defect detection model can be obtained based on the single-step multi-frame target detection model and the texture enhancement model. The texture enhancement model can be used to enhance and extract the texture features of the current concrete image.

[0126] In some embodiments, the concrete defect detection device 500 may further include a construction module, a fusion module, and a training module.

[0127] The construction module can be used for the first acquisition module 510 to construct a texture enhancement model before acquiring the current concrete image of the concrete in response to the received detection instruction; the fusion module can be used to fuse the single-step multi-frame target detection model and the texture enhancement model to obtain an initial defect detection model; the training module can be used to train the initial defect detection model to obtain a target defect detection model.

[0128] In some embodiments, a building block can include a fusion unit.

[0129] The fusion unit can be used to fuse the center difference convolution module, the horizontal difference convolution module, the vertical difference convolution module, the angle difference convolution module and the channel shuffling module to obtain a texture enhancement model.

[0130] The center difference convolution module can be used to calculate the difference in texture features between the central pixel of the current concrete image and the surrounding neighborhood to obtain a first feature map; the horizontal difference convolution module can be used to extract the horizontal texture features of the first feature map to obtain a second feature map; the vertical difference convolution module can be used to extract the vertical texture features of the second feature map to obtain a third feature map; the angular difference convolution module can be used to extract texture features at multiple angles of the third feature map to obtain a fourth feature map; the channel shuffling module can be used to enhance the cross-channel communication of the texture features of the first feature map, the texture features of the second feature map, the texture features of the third feature map, and the texture features of the fourth feature map to obtain a fifth feature map.

[0131] In some embodiments, the training module may include an acquisition unit and an input unit.

[0132] The acquisition unit can be used to acquire a corresponding sample set based on historical concrete defect images, and the sample set can at least include a training set; the input unit can be used to input the training set into the initial defect detection model for training to obtain a target defect detection model.

[0133] In some embodiments, the concrete defect detection device 500 may further include a reinforcement module.

[0134] The enhancement module can be used to input the training set into the initial defect detection model for training. Before obtaining the target defect detection model, the training set is subjected to data enhancement processing to obtain an enhanced training set.

[0135] In some embodiments, the input unit may include an input sub-unit.

[0136] The input subunit can be used to input the enhanced training set into the initial defect detection model for training to obtain the target defect detection model.

[0137] In some embodiments, the concrete defect detection device 500 may further include a second input module and a generation module.

[0138] The second input module can be used to input the first detection result into the remaining life prediction model to obtain the remaining life of the concrete when the defect detection result is the first detection result used to characterize that the current concrete image contains current defect information. The remaining life prediction model can be obtained by training a deep learning neural network model based on historical defect information; the generation module can be used to generate corresponding warning information when the remaining life of the concrete is less than or equal to the remaining life threshold.

[0139] In some embodiments, the concrete defect detection device 500 may further include a second acquisition module and a sending module.

[0140] The second acquisition module can be used to obtain the defect treatment plan corresponding to the first detection result when the defect detection result is a first detection result used to characterize that the current concrete image contains current defect information; the sending module can be used to send the defect treatment plan to the client associated with the inspection personnel.

[0141] The solution provided in this embodiment obtains a current concrete image of concrete in response to a received detection instruction, the detection instruction is used to instruct defect detection on the concrete, and the current concrete image is input into a target defect detection model to obtain a corresponding defect detection result. The target defect detection model is obtained based on a single-step multi-frame target detection model and a texture enhancement model. The texture enhancement model is used to enhance and extract texture features of the current concrete image, thereby realizing defect detection of concrete based on the target defect detection model constructed based on the single-step multi-frame target detection model and the texture enhancement model. The detection time is short, thereby improving the detection efficiency and accuracy of defect detection on concrete.

[0142] In addition, a target defect detection model is constructed based on the single-step multi-frame target detection model and the texture enhancement model, which is conducive to improving the learning ability of the target defect detection model to learn the texture features of the current concrete image, making concrete defects easier to detect, and is conducive to further improving the detection accuracy of concrete defects.

[0143] It should be noted that the various embodiments in this specification are described in a progressive manner, and each embodiment focuses on the differences from other embodiments. The same or similar parts between the various embodiments can be referred to in detail. For the device embodiments, since they are basically similar to the method embodiments, the description is relatively simple. For the relevant parts, refer to the partial description of the method embodiments. Any processing method described in the method embodiment can be implemented by the corresponding processing module in the device embodiment, and will not be repeated in detail in the device embodiment.

[0144] In addition, the functional modules in the various embodiments of the present application may be integrated into a processing module, or each module may exist physically separately, or two or more modules may be integrated into a single module. The above-mentioned integrated modules may be implemented in the form of hardware or software functional modules.

[0145] See also Figure 9 , which shows a functional block diagram of an electronic device 600 provided by an embodiment of the present application. The electronic device 600 may include one or more of the following components: a memory 610, a processor 620, and one or more applications, wherein the one or more applications may be stored in the memory 610 and configured to be executed by the one or more processors 620, and the one or more applications are configured to execute the method described in the aforementioned method embodiment.

[0146] The memory 610 may include a random access memory (RAM) or a read-only memory (ROM). The memory 610 may be used to store instructions, programs, codes, code sets, or instruction sets. The memory 610 may include a program storage area and a data storage area, wherein the program storage area may store instructions for implementing an operating system, instructions for implementing at least one function (such as receiving a detection instruction, responding to a detection instruction, obtaining a current concrete image, defect detection, inputting a current concrete image, obtaining a defect detection result, enhancing texture features, extracting texture features, constructing a texture enhancement model, fusing a single-step multi-box target detection model and a texture enhancement model, obtaining an initial defect detection model, training the initial defect detection model, obtaining a target defect detection model, fusing a module, obtaining a texture enhancement model, calculating texture feature differences, obtaining a first feature map, extracting texture features, obtaining a second feature map, obtaining a third feature map, obtaining a fourth feature map, shuffling texture features, obtaining a fifth feature map, obtaining a sample set, inputting a training set, data enhancement processing, obtaining an enhanced training set, inputting an enhanced training set, inputting a first detection result, obtaining the remaining life of concrete, training a deep learning neural network model, obtaining a remaining life prediction model, generating warning information, obtaining a defect treatment plan, and sending a defect treatment plan, etc.), instructions for implementing the following various method embodiments, etc. The data storage area can also store data created by the electronic device 600 during use (such as detection instructions, concrete, current concrete image, target defect detection model, defect detection results, single-step multi-frame target detection model, texture enhancement model, texture features, initial defect detection model, center difference convolution module, horizontal difference convolution module, vertical difference convolution module, angle difference convolution module, channel shuffling module, center pixel, surrounding neighborhood, texture feature difference, first feature map, horizontal direction, second feature map, vertical direction, third feature map, multiple angles, fourth feature map, fifth feature map, historical concrete defect images, sample set, training set, enhanced training set, current defect information, first detection result, remaining life prediction model, remaining life of concrete, historical defect information, deep learning neural network model, remaining life threshold, warning information, defect handling plan, inspection personnel and client), etc.

[0147] The processor 620 may include one or more processing cores. The processor 620 utilizes various interfaces and circuits to connect various components within the electronic device 600. It executes instructions, programs, code sets, or instruction sets stored in the memory 610, and accesses data stored in the memory 610 to perform various functions and process data within the electronic device 600. Optionally, the processor 620 may be implemented using at least one hardware form factor selected from the group consisting of a digital signal processing (DSP), a field-programmable gate array (FPGA), and a programmable logic array (PLA). The processor 620 may integrate one or a combination of a central processing unit (CPU), a graphics processing unit (GPU), and a modem. The CPU primarily processes the operating system, user interface, and application programs; the GPU is responsible for rendering and drawing display content; and the modem handles wireless communications. It is understood that the modem may not be integrated into the processor 620 and may instead be implemented separately via a communications chip.

[0148] Please refer to Figure 10 , which shows a block diagram of a computer-readable storage medium provided in an embodiment of the present application. The computer-readable storage medium 700 stores program code 710, which can be called by a processor to execute the method described in the above method embodiment.

[0149] The computer-readable storage medium 700 can be an electronic memory such as a flash memory, an EEPROM (Electrically Erasable Programmable Read-Only Memory), an EPROM, a hard disk, or a ROM. Alternatively, the computer-readable storage medium 700 includes a non-transitory computer-readable storage medium. The computer-readable storage medium 700 has storage space for program code 710 for executing any of the method steps described above. These program codes can be read from or written to one or more computer program products. The program code 710 can be compressed, for example, in a suitable form.

[0150] Please refer to Figure 11, which shows a block diagram of the structure of a computer program product 800 provided in an embodiment of the present application. The computer program product 800 includes a computer program / instructions 810, which is stored in a computer-readable storage medium of a computer device. When the computer program product 800 is executed on the computer device, the computer device's processor reads the computer program / instructions 810 from the computer-readable storage medium and executes the computer program / instructions 810, causing the computer device to perform the method described in the above method embodiment.

[0151] The solution provided in this embodiment obtains a current concrete image of concrete in response to a received detection instruction, the detection instruction is used to instruct defect detection on the concrete, and the current concrete image is input into a target defect detection model to obtain a corresponding defect detection result. The target defect detection model is obtained based on a single-step multi-frame target detection model and a texture enhancement model. The texture enhancement model is used to enhance and extract texture features of the current concrete image, thereby realizing defect detection of concrete based on the target defect detection model constructed based on the single-step multi-frame target detection model and the texture enhancement model. The detection time is short, thereby improving the detection efficiency and accuracy of defect detection on concrete.

[0152] In addition, a target defect detection model is constructed based on the single-step multi-frame target detection model and the texture enhancement model, which is conducive to improving the learning ability of the target defect detection model to learn the texture features of the current concrete image, making concrete defects easier to detect, and is conducive to further improving the detection accuracy of concrete defects.

[0153] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. A method for detecting concrete defects, characterized in that: include: acquiring a current concrete image of the concrete in response to a received inspection instruction, wherein the inspection instruction is used to instruct to perform defect inspection on the concrete; The current concrete image is input into a target defect detection model to obtain a corresponding defect detection result. The target defect detection model is obtained based on a single-step multi-frame target detection model and a texture enhancement model. The texture enhancement model is used to enhance and extract texture features of the current concrete image.

2. The concrete defect detection method according to claim 1, characterized in that: Before obtaining the current concrete image of the concrete in response to the received detection instruction, the concrete defect detection method further includes: Constructing the texture enhancement model; Fusing the single-step multi-box object detection model and the texture enhancement model to obtain an initial defect detection model; The initial defect detection model is trained to obtain the target defect detection model.

3. The concrete defect detection method according to claim 2, characterized in that: The constructing of the texture enhancement model comprises: Fusing a center difference convolution module, a horizontal difference convolution module, a vertical difference convolution module, an angle difference convolution module, and a channel shuffling module to obtain the texture enhancement model; The center difference convolution module is used to calculate the texture feature difference between the central pixel of the current concrete image and the surrounding neighborhood to obtain a first feature map; the horizontal difference convolution module is used to extract the texture features of the first feature map in the horizontal direction to obtain a second feature map; the vertical difference convolution module is used to extract the texture features of the second feature map in the vertical direction to obtain a third feature map; the angle difference convolution module is used to extract the texture features of multiple angles of the third feature map to obtain a fourth feature map; the channel shuffling module is used to enhance the cross-channel communication of the texture features of the first feature map, the texture features of the second feature map, the texture features of the third feature map and the texture features of the fourth feature map to obtain a fifth feature map.

4. The concrete defect detection method according to claim 2, characterized in that: The training of the initial defect detection model to obtain the target defect detection model includes: Acquire a corresponding sample set based on historical concrete defect images, wherein the sample set at least includes a training set; The training set is input into the initial defect detection model for training to obtain the target defect detection model.

5. The concrete defect detection method according to claim 4, characterized in that: Before inputting the training set into the initial defect detection model for training to obtain the target defect detection model, the concrete defect detection method further includes: Performing data enhancement processing on the training set to obtain an enhanced training set; The inputting the training set into the initial defect detection model for training to obtain the target defect detection model includes: The enhanced training set is input into the initial defect detection model for training to obtain the target defect detection model.

6. The concrete defect detection method according to claim 1, characterized in that: Also includes: When the defect detection result is a first detection result indicating that the current concrete image contains current defect information, inputting the first detection result into a remaining life prediction model to obtain the remaining life of the concrete, wherein the remaining life prediction model is obtained by training a deep learning neural network model based on historical defect information; When the remaining life of the concrete is less than or equal to the remaining life threshold, a corresponding warning message is generated.

7. The concrete defect detection method according to any one of claims 1 to 6, characterized in that: Also includes: When the defect detection result is a first detection result indicating that the current concrete image contains current defect information, obtaining a defect treatment solution corresponding to the first detection result; Send the defect handling solution to the client associated with the inspector.

8. A concrete defect detection device, characterized in that: include: a first acquisition module, configured to acquire a current concrete image of concrete in response to a received detection instruction, wherein the detection instruction is used to instruct to perform defect detection on the concrete; The first input module is used to input the current concrete image into a target defect detection model to obtain a corresponding defect detection result. The target defect detection model is obtained based on a single-step multi-frame target detection model and a texture enhancement model. The texture enhancement model is used to enhance and extract the texture features of the current concrete image.

9. An electronic device, characterized in that: include: Memory; one or more processors coupled to the memory; One or more application programs, wherein the one or more application programs are stored in the memory and configured to be executed by the one or more processors, and the one or more application programs are configured to execute the concrete defect detection method according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores program codes, which can be called by a processor to execute the concrete defect detection method according to any one of claims 1 to 7.

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