Strip steel inlet detection method and related equipment

Abstract

The invention discloses a strip steel inlet detection method and related equipment, and relates to the technical field of industrial automatic detection, and the method comprises the steps: obtaining image data of a target detection point in a strip steel inlet area; target detection is conducted on the image data based on a strip steel state detection model, the abnormal state of the strip steel is determined, and the abnormal state comprises the steel stacking state, the steel clamping state or the strip steel arching state; and generating a control instruction based on the abnormal state to adjust the operation action of the production line. According to the method, manual monitoring is replaced by automatic detection, the omission ratio and the misjudgment rate are reduced, response and interlocking control of the abnormal state are achieved, non-planned shutdown of a production line is effectively avoided, and continuity and high efficiency of the production process are guaranteed.

Description

Technical Field

[0001] The present application relates to the field of industrial automation detection technology, and in particular to a strip steel entrance detection method and related equipment. Background Art

[0002] During the continuous production of strip steel, abnormalities such as steel jamming, steel piling, and strip arching are prone to occur at the inlet. These problems exhibit both temporal and spatial randomness. Traditional detection methods rely primarily on manual monitoring of industrial television screens, which suffer from high rates of missed detection and significant response delays. Manual judgment is susceptible to subjective factors and struggles to track dynamic anomalies in real time, leading to frequent passive slowdowns or even shutdowns of the production line, severely impacting production efficiency. Therefore, a strip inlet detection method is urgently needed to address these technical issues. Summary of the Invention

[0003] The Summary of the Invention introduces a series of simplified concepts that will be further described in the Detailed Description of the Invention. The Summary of the Invention of this application is not intended to limit the key features and essential technical features of the claimed technical solution, nor is it intended to determine the scope of protection of the claimed technical solution.

[0004] In a first aspect, the present application provides a strip entrance detection method, comprising:

[0005] Acquire image data of target detection points in the strip entrance area;

[0006] Performing target detection on the image data based on the strip state detection model to determine abnormal states of the strip, where abnormal states include steel piling, steel jamming, or strip arching.

[0007] Based on abnormal conditions, control instructions are generated to adjust the operation of the production line.

[0008] In some embodiments, acquiring image data of a target detection point in a strip entry area includes:

[0009] Based on the preset acquisition frequency of the target industrial camera, the target industrial camera is controlled to perform image acquisition operations to generate raw image data of the target detection point;

[0010] Based on the preset image enhancement algorithm, the original image data of the target detection point is denoised and the contrast is adjusted to generate the image data of the target detection point in the strip entrance area.

[0011] In some embodiments, the strip state detection model includes a feature extraction module and an attention mechanism module. Target detection is performed on image data based on the strip state detection model to determine abnormal states of the strip, including:

[0012] Based on the image data, the global features and local features of the strip are extracted through the feature extraction module;

[0013] Based on the attention mechanism module, the weights of global features and local features are assigned to determine the attention-enhanced features of the strip;

[0014] Perform bounding box regression on the attention-enhanced features based on dynamic anchor box parameters to generate potential abnormal regions of the strip and the confidence of the potential abnormal regions;

[0015] Based on the comparison result of the confidence level of the potential abnormal area and the preset confidence threshold, the abnormal state of the strip is determined.

[0016] In some embodiments, based on the abnormal state, generating a control instruction to adjust the operation of the production line includes:

[0017] Based on the target type of the abnormal state, determining a target instruction type corresponding to the target type;

[0018] Based on the target instruction type, control instructions are generated and sent to the production line control system through the industrial communication protocol to adjust the production line operation.

[0019] In some implementations, determining a target instruction type corresponding to the target type based on the target type of the abnormal state includes:

[0020] When the abnormal state is the strip arching state, the target instruction type is determined to be a production line speed reduction instruction or a production line stop instruction;

[0021] When the abnormal state is a steel pile state or a steel stuck state, the target instruction type is determined to be a production line stop instruction.

[0022] In some embodiments, further comprising:

[0023] Based on the running speed of the strip steel, the image acquisition trigger interval of the target industrial camera is determined;

[0024] Dynamically adjust the image acquisition frequency of the target industrial camera based on the image acquisition trigger interval and the preset response time of the target industrial camera;

[0025] The adjusted image acquisition frequency is used as the preset acquisition frequency.

[0026] In some embodiments, the attention mechanism module includes a channel attention submodule and a spatial attention submodule. The attention mechanism module weights global features and local features to determine attention-enhanced features of the steel strip, including:

[0027] Based on the channel attention submodule, the weight coefficient of each feature channel in the global feature is calculated;

[0028] Based on the spatial attention submodule, the weight coefficient of each spatial position in the local feature is calculated;

[0029] The weight coefficient of each feature channel is fused with the weight coefficient of each spatial position to determine the fusion weight coefficient;

[0030] Based on the fusion weight coefficient, the attention enhancement features of the strip are generated.

[0031] In a second aspect, the present application proposes a strip steel entrance detection device, comprising:

[0032] A strip steel inlet image acquisition unit, used to acquire image data of target detection points in the strip steel inlet area;

[0033] a strip abnormal state determination unit, which performs target detection on the image data based on the strip state detection to determine the abnormal state of the strip, wherein the abnormal state includes a steel pile state, a steel stuck state, or a steel arch state;

[0034] The strip steel production line adjustment unit generates control instructions to adjust the production line operation based on abnormal conditions.

[0035] In a third aspect, an electronic device comprises: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor is configured to implement the steps of the strip entrance detection method of any one of the first aspects when executing the computer program stored in the memory.

[0036] In a fourth aspect, the present application proposes a computer-readable storage medium having a computer program stored thereon, which implements the strip entrance detection method of any one of the first aspects when the computer program is executed by a processor.

[0037] In summary, this application acquires image data from the strip entrance area and performs target detection based on a strip state detection model. This allows accurate identification of abnormal conditions such as steel piling, steel jamming, and strip arching, and then generates control instructions to adjust production line operations. This method replaces manual monitoring with automated detection, reducing missed detection and misjudgment rates. It also enables real-time response and interlocking control of abnormal conditions, effectively avoiding unplanned production line downtime and ensuring the continuity and efficiency of the production process. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] Various other advantages and benefits will become apparent to those skilled in the art upon reading the detailed description of the preferred embodiment below. The accompanying drawings are for illustration purposes only and are not to be considered as limiting the present description. The same reference symbols are used throughout the drawings to represent the same components. In the drawings:

[0039] Figure 1A schematic flow chart of a strip steel entrance detection method provided in an embodiment of the present application;

[0040] Figure 2 A schematic diagram of a strip steel entrance detection interface provided in an embodiment of the present application;

[0041] Figure 3 A schematic structural diagram of a strip steel entrance detection device provided in an embodiment of the present application;

[0042] Figure 4 A schematic structural diagram of an electronic device for detecting a strip steel entrance provided in an embodiment of the present application. DETAILED DESCRIPTION

[0043] The terms "first", "second", "third", "fourth", etc. (if any) in the specification and claims of this application and the above-mentioned drawings are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments described herein can be implemented in an order other than that illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions. For example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or units that are not clearly listed or that are inherent to these processes, methods, products or devices. The technical solutions in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the embodiments described are only part of the embodiments of the present application, not all of the embodiments.

[0044] See also Figure 1 , which is a flow chart of a strip steel entrance detection method provided in an embodiment of the present application, which may specifically include:

[0045] S110, acquiring image data of target detection points in the strip entrance area;

[0046] Exemplarily, step S110 is implemented using an industrial camera deployed at the strip entrance. Specifically, during the continuous production of the strip, the industrial camera captures images of target detection points in the entrance area based on a preset acquisition frequency. The camera's triggering logic dynamically adapts to the strip's operating speed. For example, by calculating the relationship between the strip's moving speed and the camera's response time, the image acquisition frequency is adjusted in real time to ensure coverage of the dynamically changing detection area. Furthermore, the camera is equipped with a high-resolution lens and auxiliary light source (such as an LED linear light source) to ensure image clarity and illumination uniformity, preventing image quality degradation due to environmental interference.

[0047] The collected raw image data undergoes preprocessing to optimize subsequent analysis. For example, denoising algorithms are used to eliminate interference from dust and reflections in industrial environments, while contrast enhancement techniques are used to highlight details such as strip edges and abnormal areas. This preprocessed image data serves as input for subsequent object detection, providing a highly reliable visual basis for identifying abnormal conditions. This step, through the combination of hardware deployment and dynamic adjustment mechanisms, provides a stable and efficient source of image data for the entire inspection process.

[0048] S120, performing target detection on the image data based on the strip steel state detection model to determine an abnormal state of the strip steel, wherein the abnormal state includes a steel pile state, a steel stuck state, or a steel arched state;

[0049] Exemplarily, step S120 uses a deep learning model to perform feature analysis and anomaly identification on the preprocessed image data. The strip condition detection model integrates global and local feature extraction capabilities and employs an attention mechanism to dynamically weight key regions, thereby enhancing the model's sensitivity to anomalies. Based on the image data, the model generates bounding boxes and confidence levels for potential anomaly areas. It then filters valid detection results using a preset threshold and ultimately outputs the type of anomaly condition in the strip. Through multi-level feature fusion and an adaptive learning mechanism, the model achieves precise localization of anomalies in complex scenarios.

[0050] The strip condition detection model dynamically adjusts its detection strategy to adapt to different operating conditions. For example, in scenarios involving high-speed strip operation, the model optimizes feature extraction efficiency and bounding box regression accuracy to ensure real-time and reliable detection results. Furthermore, negative sample augmentation technology is introduced during model training to improve generalization of rare abnormal patterns and avoid missed detections or misjudgments. This design enables the detection model to operate stably in complex industrial environments, providing highly reliable evidence for abnormal condition determination on production lines.

[0051] S130. Based on the abnormal state, generate a control instruction to adjust the operation of the production line.

[0052] For example, when an abnormal strip state is detected, corresponding control instructions are generated based on the mapping relationship between the abnormality type and the preset control strategy. For example, a piled or stuck steel state directly triggers an emergency shutdown command for the production line to immediately prevent the spread of the abnormality; a bulging strip state determines the risk level based on the height of the bulge, and selects a speed reduction command or a shutdown command to balance production efficiency and safety requirements. The generated instructions are transmitted to the production line control system in real time via an industrial communication protocol, driving the actuator to adjust the production line operating parameters (such as roller speed and tension control), thereby quickly eliminating the interference of abnormal conditions on the production process.

[0053] Through an automated interlocking control mechanism, a direct link between abnormal conditions and production line operations is achieved. Compared to traditional manual intervention, this application shortens response time and avoids production line losses caused by delayed manual judgment. At the same time, the dynamic adaptability of control instructions further optimizes the operational stability of the production line, minimizing unplanned downtime while ensuring safety, and ensuring the efficiency and economy of continuous production.

[0054] In summary, the embodiment of the present application achieves significant technical effects by acquiring image data of target detection points in the strip entrance area, performing target detection based on the strip state detection model, and generating control instructions to adjust the production line operation. The industrial camera accurately captures the strip operation status based on the dynamically adjusted acquisition frequency, and improves the image quality by combining denoising and contrast enhancement preprocessing, providing a reliable data basis for anomaly detection. The deep learning model integrates global and local feature extraction capabilities, dynamically allocates weights through the attention mechanism, enhances the recognition sensitivity of abnormal areas such as steel piles, steel jams, and strip arches, and optimizes the bounding box regression accuracy by combining dynamic anchor frame parameters to ensure high confidence in the detection results. After the abnormal state is determined, a speed reduction or shutdown instruction is generated according to the type difference, and the production line control system is linked in real time through the industrial communication protocol, which greatly shortens the response delay and avoids missed detection and misjudgment caused by manual intervention. The embodiment of the present application effectively reduces the frequency of unplanned shutdowns, improves the stability and continuity of production line operation, and takes into account safety and production efficiency at the same time, providing a solution for industrial automation detection.

[0055] In some examples, acquiring image data of a target detection point in a strip entry area includes:

[0056] Based on the preset acquisition frequency of the target industrial camera, the target industrial camera is controlled to perform image acquisition operations to generate raw image data of the target detection point;

[0057] Based on the preset image enhancement algorithm, the original image data of the target detection point is denoised and the contrast is adjusted to generate the image data of the target detection point in the strip entrance area.

[0058] Exemplarily, image data acquisition of the strip entrance area is achieved through multiple target industrial cameras deployed at the entrance section of the production line. The target industrial camera performs image acquisition operations according to a preset acquisition frequency. The preset acquisition frequency is determined based on the adjusted image acquisition frequency, and the image acquisition trigger interval is calculated based on the running speed of the strip. The image acquisition frequency of the target industrial camera is dynamically adjusted in combination with the preset response time parameters of the target industrial camera (for example, the time it takes for the camera to receive a trigger signal and complete a single image acquisition) to ensure that the acquisition interval matches the moving speed of the strip. For example, when the strip runs at high speed, the trigger interval is shortened proportionally to avoid incomplete coverage of the detection area due to strip displacement; when the strip runs at low speed, the trigger interval is extended accordingly to avoid overloading of camera resources. Please refer to Figure 2 , a schematic diagram of a strip entrance inspection interface provided in an embodiment of the present application, wherein the number of target industrial cameras can be set to seven; target industrial cameras are deployed at preset inspection points in the strip entrance area (upper channel, lower channel, and behind the common pinch rollers). Each inspection point corresponds to at least one high-resolution industrial camera and is equipped with an auxiliary light source (such as an LED linear light source) to eliminate the effects of uneven ambient lighting. A periodic trigger signal is generated based on the adjusted image acquisition frequency, driving the image sensor to capture raw image data of the strip entrance area and transmit it to the server in real time via a high-speed transmission interface (such as optical fiber or Gigabit Ethernet).

[0059] The original image data needs to be preprocessed to improve the accuracy of subsequent target detection. First, a denoising algorithm (such as non-local mean denoising or Gaussian filtering) is used to eliminate the interference of dust, reflection or sensor noise in the industrial environment on the image quality. Secondly, contrast enhancement technology (such as histogram equalization or adaptive gamma correction) is used to optimize the detailed features of the strip edges and abnormal areas in the image to ensure the target recognizability in low-contrast scenes. For example, for the arched area of ​​the strip, the difference between the arch height and the background is highlighted by enhancing the local contrast; for the piled or stuck steel area, the texture features are enhanced to distinguish between abnormal accumulation and normal strip surface. The preprocessed image data retains the geometric shape and abnormal features of the strip, providing high signal-to-noise ratio input for the subsequent deep learning model. The parameters of the image enhancement algorithm (such as filter kernel size and contrast gain coefficient) are set according to the actual situation to ensure that it adapts to the detection requirements of different lighting conditions and strip materials.

[0060] In some examples, the method for determining the preset acquisition frequency includes:

[0061] Based on the running speed of the strip steel, the image acquisition trigger interval of the target industrial camera is determined;

[0062] Based on the image acquisition trigger interval and the preset response time of the target industrial camera, the image acquisition frequency of the target industrial camera is dynamically adjusted; and the adjusted image acquisition frequency is used as the preset acquisition frequency.

[0063] Exemplarily, the image acquisition trigger interval = preset reference distance / strip running speed, where the preset reference distance is determined by the field of view coverage and detection accuracy requirements of the target industrial camera. For example, if the camera's horizontal field of view coverage is 500mm, and it is necessary to ensure that the strip displacement in two adjacent frames does not exceed 50mm to avoid missed detection, the preset reference distance is set to 50mm. When the strip runs at a speed of 2m / s, the trigger interval is calculated to be 25ms. By real-time monitoring of the strip running speed (for example, obtained through an encoder or speed sensor), the trigger interval parameters are dynamically updated to ensure seamless coverage of the detection area at different speeds.

[0064] The preset response time of the target industrial camera refers to the total time it takes from receiving the trigger signal to completing a single image acquisition and transmission (for example, 10ms). The dynamic adjustment logic process is as follows: if the calculated trigger interval is greater than the preset response time, the image acquisition trigger interval is directly used as the trigger interval. At this time, the camera has sufficient time to complete a single acquisition. To ensure that the camera has sufficient time to complete a single acquisition, the adjusted image acquisition frequency is 1 / image acquisition trigger interval; if the trigger interval is less than or equal to the preset response time, the trigger interval is adjusted to an integer multiple k of the preset response time (take 1.5 times, that is, 15ms) to avoid image loss or blurring due to the trigger frequency exceeding the camera processing capability, so as to ensure that the camera completes the current acquisition before triggering the next operation. The adjusted image acquisition frequency is 1 / (k×preset response time).

[0065] In some examples, the strip state detection model includes a feature extraction module and an attention mechanism module. The strip state detection model performs object detection on image data to determine abnormal states of the strip, including:

[0066] Based on the image data, the global features and local features of the strip are extracted through the feature extraction module;

[0067] Based on the attention mechanism module, the weights of global features and local features are assigned to determine the attention-enhanced features of the strip;

[0068] Perform bounding box regression on the attention-enhanced features based on dynamic anchor box parameters to generate potential abnormal regions of the strip and the confidence of the potential abnormal regions;

[0069] Based on the comparison result of the confidence level of the potential abnormal area and the preset confidence threshold, the abnormal state of the strip is determined.

[0070] For example, after acquiring the image data of the strip entrance area, the feature extraction module of the strip state detection model performs multi-scale feature analysis on the image data. The feature extraction module uses a deep convolutional neural network (YOLOv5 architecture) to extract the global features (such as overall shape, running direction) and local features (such as edge texture, abnormal area details) of the strip from the image through multi-layer convolution and pooling operations. The global features capture the macroscopic morphology of the strip through a high-level network, while the local features retain the pixel-level details of the abnormal area through a shallow network. For example, for the arched state of the strip, the global features are used to identify the overall bending trend of the strip, while the local features focus on the geometric deformation and surface texture changes of the arched area. The output of the feature extraction module is a feature map containing different levels of abstraction, which provides a data basis for the subsequent attention mechanism to assign weights.

[0071] After completing feature extraction, the attention mechanism module dynamically assigns weights to global features and local features to generate attention-enhanced features. The attention mechanism module consists of a channel attention submodule and a spatial attention submodule; the channel attention submodule calculates the weight coefficients of each feature channel through global average pooling and a fully connected layer, highlighting the channel features that contribute most to anomaly detection (for example, the high-frequency texture features in the steel jam area have higher channel weights); the spatial attention submodule calculates the weight coefficients of each spatial position in the feature map through convolution operations to enhance the spatial significance of abnormal areas (such as steel pile accumulation points and arch vertices); weight fusion is to fuse the channel weight coefficients with the spatial weight coefficients in a preset ratio to generate a fusion weight coefficient matrix, and weight the original feature map based on this matrix to obtain attention-enhanced features. This step suppresses background noise interference through adaptive weight allocation and improves the model's ability to focus on abnormal areas.

[0072] After acquiring the attention-enhanced features, the model performs bounding box regression on the feature map using dynamic anchor box parameters to generate potential anomaly regions in the strip and their confidence levels. Based on the geometric features of the anomaly regions in the training dataset, a K-means clustering algorithm is used to generate anchor box sizes and aspect ratios that match the anomaly shapes. The CIoU loss function is used to optimize the positioning accuracy of the anchor boxes, and the coordinate information of candidate anomaly regions is generated by predicting the center coordinate offset, width, and height scaling factors of the bounding boxes. Based on the classification scores of the candidate anomaly regions in the feature map and the bounding box regression accuracy, the confidence levels of the candidate regions are comprehensively calculated to reflect the probability of an anomaly. For example, the confidence level of a stuck steel region is determined by its matching degree to the pre-set stuck steel shape and the completeness of the bounding box coverage.

[0073] The confidence of the potential abnormal area is compared with the preset confidence threshold to screen out the valid detection results. The preset confidence threshold is determined by ROC curve analysis on the model validation set to balance the detection accuracy and false alarm rate. For candidate boxes with a confidence greater than or equal to the threshold, the specific abnormality type is determined based on its bounding box coordinates and category label (steel pile, steel jam, arch); for candidate boxes with a confidence less than the threshold, they are determined to be background interference and are eliminated. When multiple overlapping candidate boxes are detected, the Soft-NMS algorithm is used to suppress redundant detection, that is, the confidence of the overlapping boxes is dynamically reduced according to the confidence and IoU value, and only the detection results with the highest confidence are retained. Finally, the abnormal state type, position coordinates and confidence score of the strip are output to provide a reliable basis for the generation of production line control instructions.

[0074] In some instances, based on abnormal conditions, control instructions are generated to adjust production line operations, including:

[0075] Based on the target type of the abnormal state, determining a target instruction type corresponding to the target type;

[0076] Based on the target instruction type, control instructions are generated and sent to the production line control system through the industrial communication protocol to adjust the production line operation.

[0077] Exemplarily, when an abnormal state of the strip is detected, the corresponding target instruction type is determined based on the mapping relationship between the target type of the abnormal state (including steel piling state, steel jamming state or strip arching state) and the preset control strategy. Specifically, if the abnormal state is a strip arching state, the vertical height parameter of the arching area is further calculated based on the bounding box coordinates of the potential abnormal area, and compared with the first preset height threshold (e.g., 100 mm) and the second preset height threshold (e.g., 50 mm). When the vertical height parameter is greater than or equal to the first preset height threshold, it is determined to be a high-risk arching and an emergency shutdown instruction for the production line is generated; when the vertical height parameter is less than the first preset height threshold but greater than or equal to the second preset height threshold, it is determined to be a potential risk and a production line speed reduction instruction is generated; if the abnormal state is a steel piling state or a steel jamming state, it is determined to be an emergency abnormality and a production line shutdown instruction is generated; the above-mentioned judgment logic is implemented through a preset rule base to ensure a strict correspondence between the abnormal type and the instruction type, so that manual intervention can be performed while maintaining production. The first preset height threshold and the second preset height threshold are set according to historical data and process safety specifications to ensure the adaptability and safety of the control strategy.

[0078] Based on the target instruction type, standardized control instructions are generated through industrial communication protocols (such as OPC protocols). The control instructions include instruction codes, target device addresses, and execution parameters (such as the shutdown code "EMG_STOP" or the speed reduction parameter "SPEED_30%"). After the instruction is generated, it is sent to the production line control system (L1 system) through a real-time communication interface (such as industrial Ethernet) to adjust the operation of the production line. When a production line shutdown instruction is generated, the control instruction includes a shutdown signal and abnormal position information, driving the L1 system to immediately cut off the roller power and start the mechanical brake device to ensure that the strip stops within the set time and synchronously triggers the sound and light alarm; when a production line speed reduction instruction is generated, the control instruction includes a target speed reduction ratio (for example, reduced to 40% of the current speed) and a duration parameter, driving the L1 system to adjust the roller speed according to the preset gradient and activate the early warning prompt. The generation and transmission process of the control instruction follows the preset communication cycle to ensure that the response delay is lower than the safety threshold of the production line. Simultaneously, the execution status of control commands and exception details are updated in real time to the human-machine interface (HMI), including exception type, command type, execution timestamp, and action suggestions, providing decision support for operators. These steps enable real-time closed-loop control of exception status and production line operations, improving response efficiency and operational safety.

[0079] See also Figure 3 , is a schematic structural diagram of a strip steel entrance detection device provided in an embodiment of the present application, comprising:

[0080] The strip steel entrance image acquisition unit 21 acquires image data of target detection points in the strip steel entrance area;

[0081] The strip abnormal state determination unit 22 performs target detection on the image data based on the strip state detection to determine the abnormal state of the strip, which includes a steel pile state, a steel stuck state, or a steel arch state.

[0082] The strip steel production line adjustment unit 23 generates control instructions based on the abnormal state to adjust the production line operation.

[0083] See also Figure 4 An embodiment of the present application also provides an electronic device 300, including a memory 310, a processor 320, and a computer program 311 stored in the memory 310 and executable on the processor. When the processor 320 executes the computer program 311, the steps of any method for strip entrance detection are implemented.

[0084] Since the electronic device introduced in this embodiment is the equipment used to implement a strip entrance detection device in the embodiment of this application, based on the method introduced in the embodiment of this application, technical personnel in this field can understand the specific implementation of the electronic device of this embodiment and its various variations. Therefore, how the electronic device implements the method in the embodiment of this application will not be introduced in detail here. As long as the equipment used by technical personnel in this field to implement the method in the embodiment of this application falls within the scope of protection of this application.

[0085] During the specific implementation process, when the computer program 311 is executed by the processor, any implementation method of the embodiments corresponding to the first aspect can be implemented.

[0086] It should be noted that, in the above embodiments, the description of each embodiment has its own focus. For parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0087] Those skilled in the art will appreciate that the embodiments of the present application may provide methods, systems, or computer program products. Therefore, the present application may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Furthermore, the present application may take the form of a computer program product implemented on one or more computer-readable storage media (including but not limited to magnetic disk storage, CD-ROM, optical storage, etc.) containing computer-readable program code.

[0088] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems) and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded computer or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0089] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 a process or multiple processes and / or boxes Figure 1 The function specified in one or more boxes.

[0090] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0091] The present application also provides a computer program product, which includes computer software instructions. When the computer software instructions are executed on a processing device, the processing device executes Figure 1 The process of a strip steel entrance detection method in the corresponding embodiment.

[0092] A computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, the process or function according to the embodiment of the present application is generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions can be transmitted from one website, computer, server or data center to another website, computer, server or data center via wired or wireless means. The computer-readable storage medium can be any available medium that a computer can store or a data storage device such as a server or data center that includes one or more available media integrated therein. The available medium can be a magnetic medium, an optical medium or a semiconductor medium, etc.

[0093] Those skilled in the art will clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0094] In the several embodiments provided in this application, it should be understood that the disclosed devices, apparatuses and methods can be implemented in other ways. For example, the device embodiments described above are merely schematic. For example, the division of units is only a logical function division. In actual implementation, there may be other division methods, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be an indirect coupling or communication connection through some interface, device or unit, which can be electrical, mechanical or other forms.

[0095] Units described as separate components may or may not be physically separate, and components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of these units may be selected to achieve the purpose of this embodiment according to actual needs.

[0096] In addition, the functional units in the various embodiments of the present application may be integrated into a processing unit, or each unit may exist physically separately, or two or more units may be integrated into a single unit. The above-mentioned integrated units may be implemented in the form of hardware and / or software functional units.

[0097] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes a number of instructions for enabling a computer device to execute all or part of the steps of the various embodiments of the present application. The aforementioned storage medium includes: various media that can store program code, such as a USB flash drive, a mobile hard disk, a read-only memory, a magnetic disk, or an optical disk.

[0098] 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 deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present application.

[0099] Although the preferred embodiments of this specification have been described, those skilled in the art may make additional changes and modifications to these embodiments once they have learned the basic creative concepts. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications that fall within the scope of this specification.

[0100] Obviously, those skilled in the art may make various changes and modifications to this specification without departing from the spirit and scope of this specification. Thus, if such changes and modifications fall within the scope of the claims of this specification and their equivalents, this specification is intended to include such changes and modifications.

Claims

1. A strip steel entrance detection method, characterized in that: include: Acquire image data of target detection points in the strip entrance area; performing target detection on the image data based on a strip steel state detection model to determine an abnormal state of the strip steel, wherein the abnormal state includes a steel pile state, a steel stuck state, or a steel arched state; Based on the abnormal state, a control instruction is generated to adjust the operation of the production line.

2. The method according to claim 1, characterized in that The acquiring of image data of target detection points in the strip steel entrance area includes: Based on the preset acquisition frequency of the target industrial camera, the target industrial camera is controlled to perform image acquisition operations to generate raw image data of the target detection point; Based on a preset image enhancement algorithm, the original image data of the target detection point is denoised and the contrast is adjusted to generate image data of the target detection point in the strip entrance area.

3. The method according to claim 1, characterized in that The strip state detection model includes a feature extraction module and an attention mechanism module. The strip state detection model is used to perform target detection on the image data to determine the abnormal state of the strip, including: Based on the image data, extracting the global features of the steel strip and the local features of the steel strip by the feature extraction module; Based on the attention mechanism module, weights are assigned to the global features and the local features to determine attention-enhanced features of the steel strip; Performing bounding box regression on the attention enhancement feature based on dynamic anchor box parameters to generate a potential abnormal area of ​​the steel strip and a confidence score of the potential abnormal area; Based on the comparison result of the confidence of the potential abnormal area and the preset confidence threshold, the abnormal state of the steel strip is determined.

4. The method according to claim 1, wherein The generating of a control instruction based on the abnormal state to adjust the operation of the production line includes: Based on the target type of the abnormal state, determining a target instruction type corresponding to the target type; Based on the target instruction type, the control instruction is generated, and the control instruction is sent to the production line control system through an industrial communication protocol to adjust the production line operation action.

5. The method according to claim 4, characterized in that The determining, based on the target type of the abnormal state, a target instruction type corresponding to the target type, includes: When the abnormal state is the strip arching state, determining the target instruction type to be a production line speed reduction instruction or a production line shutdown instruction; When the abnormal state is the steel piling state or the steel stuck state, the target instruction type is determined to be the production line shutdown instruction.

6. The method according to claim 2, characterized in that Also includes: Determining an image acquisition trigger interval of the target industrial camera based on a running speed of the steel strip; Dynamically adjusting the image acquisition frequency of the target industrial camera based on the image acquisition trigger interval and the preset response time of the target industrial camera; The adjusted image acquisition frequency is used as the preset acquisition frequency.

7. The method according to claim 3, characterized in that The attention mechanism module includes a channel attention submodule and a spatial attention submodule. Based on the attention mechanism module, weights are assigned to the global features and the local features to determine the attention enhancement features of the steel strip, including: Based on the channel attention submodule, calculate the weight coefficient of each feature channel in the global feature; Based on the spatial attention submodule, calculating the weight coefficient of each spatial position in the local feature; Fusing the weight coefficients of the feature channels with the weight coefficients of the spatial positions to determine a fusion weight coefficient; Based on the fusion weight coefficient, an attention enhancement feature of the steel strip is generated.

8. A strip steel entrance detection device, characterized in that: include: A strip steel inlet image acquisition unit, used to acquire image data of target detection points in the strip steel inlet area; a steel strip abnormal state determining unit, which performs target detection on the image data based on the steel strip state detection to determine the abnormal state of the steel strip, wherein the abnormal state includes a steel pile state, a steel stuck state, or a steel strip arch state; The strip steel production line adjustment unit generates a control instruction based on the abnormal state to adjust the operation of the production line.

9. An electronic device comprising: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor is configured to implement the steps of the strip entrance detection method according to any one of claims 1 to 7 when executing the computer program stored in the memory.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the strip entrance detection method according to any one of claims 1 to 7 is implemented.

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