Scrap steel stock unloading tracking and scrap steel pile falling point identification method

Abstract

The invention discloses a waste steel stock unloading tracking and waste steel pile falling point identification method. According to the method, a plurality of cameras are mounted in different directions in a waste steel warehouse plant to comprehensively monitor a waste steel material pile. And calculating a transformation matrix between the images through feature point detection and matching, and eliminating seams by adopting a multi-band fusion splicing algorithm to form an integral lossless picture covering the whole scrap steel material pile. A real-time processing system developed by an OpenCV tool is combined, the performance is accelerated and optimized through a GPU, nondestructive coverage of panoramic monitoring of the scrap steel material pile is achieved, and the drop point of the scrap steel material pile is recognized in real time. In the waste steel unloading process, the system monitors the falling point of waste steel in real time through the camera, quality inspection is conducted, when foreign matter or abnormal conditions are detected, a crown block driver is reminded to conduct on-site secondary confirmation through the on-site signal lamp, and quality inspection tracking and control of the whole process are achieved.

Description

Technical Field

[0001] This application relates to the field of image recognition technology, and in particular to a method for tracking the unloading of scrap steel inventory and identifying the landing point of scrap steel piles. Background Technology

[0002] Inside the scrap steel warehouse, different piles of scrap steel are divided. Multiple camera devices are set up on site to fully cover the location of the scrap steel piles. The server will then stitch together the overlapping areas of all the cameras to create a seamless image that covers the entire location of the scrap steel piles.

[0003] When a new scrap metal truck arrives to unload, a scrap grabber or unloading device removes the scrap metal from the truck and places it in the designated pile location. When the unloading device enters the monitoring range of the entire scrap metal pile, the service will track its movement and direction in real time, following its placement of the scrap metal. Photos will be taken of the scrap metal at the unloading point, and newly added or changed scrap metal in the pile location will be identified. The system will then assess the dust levels at the point of impact and whether the scrap metal contains any enclosed components or other prohibited items. This entire process will be simultaneously and in real-time transmitted to the lobby monitoring platform and the overhead crane operator's terminal.

[0004] The existing solution only conducts quality inspection during the unloading process of scrap steel, and cannot conduct quality inspection on the already grabbed scrap steel. It cannot accurately quantify the impurities and dust in the scrap steel, and there is a high possibility that non-compliant parts may not be detected inside the truck. The solution is to conduct secondary confirmation at the scrap steel unloading point while inspecting inside the truck, so as to reduce the impact of non-compliant parts on the steelmaking process. Summary of the Invention

[0005] Based on this, the embodiments of this application provide a method for tracking the unloading of scrap steel inventory and identifying the landing point of scrap steel piles, which can effectively identify and monitor the landing point of scrap steel piles, thereby improving the efficiency and accuracy of scrap steel management.

[0006] Firstly, a method for tracking the unloading of scrap steel inventory and identifying the landing point of scrap steel stockpiles is provided, the method comprising:

[0007] The scrap steel warehouse is divided into different scrap steel pile areas, and multiple cameras are installed in different locations in each area. The installation of multiple cameras in different locations in each area includes calibrating the installed cameras, including internal and external parameter calibration, to ensure that the images captured by each camera can accurately reflect the actual location and status of the scrap steel pile.

[0008] Images captured by all cameras are stitched together to form a complete, lossless image covering the entire scrap steel pile. Specifically, this involves calculating the transformation matrix between images through feature point detection and matching, and using a multi-band fusion stitching algorithm to eliminate seams.

[0009] A real-time processing system was developed using OpenCV tools, and its performance was optimized through GPU acceleration to achieve lossless coverage of panoramic monitoring of scrap steel piles and to identify the landing points of scrap steel piles in real time.

[0010] During the unloading of scrap steel, the falling point of the scrap steel is monitored in real time by cameras for quality inspection. When foreign objects or abnormalities are detected, the crane operator is reminded by on-site signal lights to conduct a secondary on-site confirmation, thus realizing full-process quality inspection tracking and control.

[0011] Optionally, multiple cameras may be installed at different locations in each area. These cameras may include at least a high-definition camera, an infrared camera, and a thermal imaging camera. Specifically, the high-definition camera may be an 8-megapixel high-definition camera, and the installation height may be set to 8m-10m.

[0012] Optionally, the images captured by all the cameras are stitched together to form a complete, undamaged image covering the entire scrap metal pile, specifically including:

[0013] The captured image is preprocessed; wherein the preprocessing operation includes at least grayscale conversion, binarization, and edge detection.

[0014] The preprocessed image data is fused using either a weighted average method or a Bayesian fusion method, with different weights assigned based on the reliability and importance of each data type. Among these methods, a multi-band fusion algorithm is employed to improve the quality and stability of the stitched image by fusing images across different frequency bands.

[0015] Optionally, a real-time processing system can be developed using OpenCV tools, including setting up an anomaly detection mechanism that can identify and remove abnormal data caused by equipment failure or environmental interference; wherein, the anomaly detection mechanism is based on machine learning algorithms and can automatically learn and identify the feature patterns of abnormal data, thereby achieving rapid and accurate detection of abnormal data;

[0016] The real-time processing system also includes multi-threaded and multi-task processing, which can process image data from multiple cameras simultaneously, improving the system's real-time performance and response speed.

[0017] Optionally, the falling point of the scrap steel can be monitored in real time via camera for quality inspection, including:

[0018] Collect image data of the scrap steel drop points;

[0019] The acquired image data is preprocessed, including grayscale conversion, binarization, and edge detection;

[0020] Machine learning algorithms are used to analyze the preprocessed image data and extract the feature information of scrap steel, including the type, size and weight of the scrap steel;

[0021] Based on the extracted feature information, analysis results are generated, and the quality of scrap steel is determined according to preset thresholds and rules to determine whether it meets the standards.

[0022] If the analysis results indicate the presence of foreign objects or abnormalities in the scrap steel, record the relevant information and trigger the alarm mechanism.

[0023] Optionally, on-site signal lights can be used to remind the crane operator to conduct a secondary on-site confirmation, enabling full-process quality inspection tracking and control, specifically including:

[0024] The analysis results and alarm information are fed back to the crane operator via on-site signal lights or terminal equipment; the feedback information includes operating instructions, safety tips, and information on the type, size, and weight of the scrap steel.

[0025] Based on feedback from the crane operator, the system records the results of the secondary confirmation and updates the scrap steel quality inspection record; if an anomaly is found during the secondary confirmation, the relevant information is sent to the control center.

[0026] Secondly, a scrap steel inventory unloading tracking and scrap steel stockpile landing point identification system is provided, the system comprising:

[0027] The camera module is used to divide the scrap steel storage area into different scrap steel pile areas and install multiple cameras in different locations in each area. The installation of multiple cameras in different locations in each area includes calibrating the installed cameras, including internal and external parameter calibration, to ensure that the images captured by each camera can accurately reflect the actual location and status of the scrap steel pile.

[0028] The stitching module is used to stitch together images captured by all cameras to form a complete, lossless image covering the entire scrap steel pile. Specifically, it includes calculating the transformation matrix between images through feature point detection and matching, and eliminating seams using a multi-band fusion stitching algorithm.

[0029] The recognition module is used to develop a real-time processing system in conjunction with OpenCV tools. It optimizes performance through GPU acceleration, achieves non-destructive coverage of panoramic monitoring of scrap steel piles, and performs real-time recognition of the landing points of scrap steel piles.

[0030] The feedback module is used to monitor the falling point of scrap steel in real time through cameras during the scrap steel unloading process and to conduct quality inspection. When foreign objects or abnormalities are detected, the on-site signal lights remind the crane operator to conduct a secondary on-site confirmation, realizing full-process quality inspection tracking and control.

[0031] Thirdly, an electronic device is provided, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement any of the methods described in the first aspect above.

[0032] Fourthly, a computer-readable storage medium is provided having a computer program stored thereon, which, when executed by a processor, implements any of the methods described in the first aspect above.

[0033] Fifthly, a computer program product is provided, on which a computer program is stored, wherein the computer program, when executed by a processor, implements any of the methods described in the first aspect above.

[0034] The beneficial effects of the technical solutions provided in this application include at least the following:

[0035] (1) By installing multiple high-definition, infrared, and thermal imaging cameras at different locations within the scrap steel warehouse, and combining them with image stitching technology, a panoramic, non-destructive coverage of the scrap steel pile is achieved. This not only improves the efficiency of scrap steel management but also accurately identifies the location and quality characteristics of the scrap steel, effectively reducing the impact of non-compliant parts on the steelmaking process and ensuring the smooth operation of the production process.

[0036] (2) During the unloading of scrap steel, the falling point of the scrap steel is monitored in real time and quality is inspected. When foreign objects or abnormalities are detected, the crane operator is reminded by the on-site signal lights to conduct a second on-site confirmation. This realizes full-process tracking and control from unloading to quality inspection, effectively avoiding missed inspections and false inspections, and improving the safety and reliability of production.

[0037] (3) The real-time processing system developed using OpenCV tools, combined with GPU acceleration to optimize performance, can quickly process image data from multiple cameras, achieving lossless coverage of panoramic monitoring of scrap steel piles. The system also has an anomaly detection mechanism and adaptive learning capabilities, which can automatically identify and remove abnormal data, further improving the accuracy and stability of the system and reducing the cost of manual intervention. Attached Figure Description

[0038] To more clearly illustrate the embodiments of this application or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are merely exemplary, and those skilled in the art can derive other embodiments based on the provided drawings without creative effort.

[0039] Figure 1 A flowchart illustrating the steps of a method for tracking the unloading of scrap steel inventory and identifying the landing point of scrap steel piles, provided in this application embodiment;

[0040] Figure 2 A block diagram of a scrap steel inventory unloading tracking and scrap steel stockpile landing point identification system provided in this application embodiment;

[0041] Figure 3 This is a schematic diagram of an electronic device provided in an embodiment of this application. Detailed Implementation

[0042] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0043] In the description of this application, the terms "comprising," "having," and any variations thereof are intended to cover non-exclusive inclusion, such as a process, method, system, product, or apparatus that includes a series of steps or units, not necessarily limited to those steps or units that are expressly listed, but may also include other steps or units that are not expressly listed but are inherent to these processes, methods, products, or apparatuses, or steps or units added based on further optimizations conceived in this application.

[0044] Inside the scrap steel warehouse, different piles of scrap steel are divided. Multiple camera devices are set up on site to fully cover the location of the scrap steel piles. The server will then stitch together the overlapping areas of all the cameras to create a seamless image that covers the entire location of the scrap steel piles.

[0045] When a new scrap metal truck arrives to unload, a scrap grabber or unloading device removes the scrap metal from the truck and places it in the designated pile location. When the unloading device enters the monitoring range of the entire scrap metal pile, the service will track its movement and direction in real time, following its placement of the scrap metal. Photos will be taken of the scrap metal at the unloading point, and newly added or changed scrap metal in the pile location will be identified. The system will then assess the dust levels at the point of impact and whether the scrap metal contains any enclosed components or other prohibited items. This entire process will be simultaneously and in real-time transmitted to the lobby monitoring platform and the overhead crane operator's terminal.

[0046] The existing solution only conducts quality inspection during the unloading process of scrap steel, and cannot conduct quality inspection on the already grabbed scrap steel. It cannot accurately quantify the impurities and dust in the scrap steel, and there is a high possibility that non-compliant parts may not be detected inside the truck. The solution is to conduct secondary confirmation at the scrap steel unloading point while inspecting inside the truck, so as to reduce the impact of non-compliant parts on the steelmaking process.

[0047] It can be seen that the existing technology has the following drawbacks:

[0048] 1. It is impossible to inspect the quality of the already collected scrap steel.

[0049] 2. Inspecting the quality inside the vehicle may result in some items being missed.

[0050] 3. The lack of dust detection during the overall scrap steel quality inspection process is due to the fact that a large amount of dust is not generated during the handling process.

[0051] Please refer to Figure 1 The diagram illustrates a flowchart of a method for tracking the unloading of scrap steel inventory and identifying the landing point of scrap steel stockpiles, provided in an embodiment of this application. This method may include the following steps:

[0052] S1. Divide the scrap steel storage area into different scrap steel pile areas and install multiple cameras in different locations in each area.

[0053] This involves installing multiple cameras at different locations in each area, including calibrating the installed cameras, including internal and external parameter calibration, to ensure that the images captured by each camera accurately reflect the actual location and condition of the scrap steel pile.

[0054] In this step, the scrap steel warehouse is divided into different scrap steel pile areas based on the distribution of the scrap steel stockpiles. Multiple cameras are installed at different locations within each area to ensure comprehensive coverage of the scrap steel stockpiles. The cameras are installed at a height of 8m-10m to ensure a clear view of the scrap steel stockpiles. The installed cameras include 8-megapixel high-definition cameras, infrared cameras, and thermal imaging cameras to adapt to different environmental conditions and inspection requirements.

[0055] After installation, each camera is calibrated, including intrinsic and extrinsic parameter calibration. Intrinsic parameter calibration mainly determines parameters such as the camera's focal length and optical center, while extrinsic parameter calibration determines the camera's position and orientation relative to the scrap metal pile. Calibration ensures that the images captured by each camera accurately reflect the actual position and state of the scrap metal pile. Calibration boards and algorithms, such as the Zhang Zhengyou calibration method, can be used during the calibration process to ensure accuracy and reliability.

[0056] S2 stitches together the images captured by all the cameras to form a complete, undamaged image covering the entire scrap metal pile.

[0057] Specifically, this includes calculating the transformation matrix between images through feature point detection and matching, and eliminating seams using a multi-band fusion stitching algorithm.

[0058] In this step, images captured by all cameras are stitched together to form a complete, lossless image covering the entire scrap metal pile. First, the captured images undergo preprocessing operations, including grayscale conversion, binarization, and edge detection, to enhance their feature information. The preprocessed images are then used to calculate the transformation matrix between images through feature point detection and matching. SIFT or SURF algorithms can be used to detect feature points, and the RANSAC algorithm can be used to calculate the transformation matrix between images to ensure the accuracy of the stitching.

[0059] Then, a multi-band fusion stitching algorithm is used to eliminate seams. This algorithm improves the quality and stability of the stitched image by fusing images across different frequency bands. During the stitching process, it is also necessary to address issues such as illumination consistency, parallax effects, and dynamic scene handling to ensure the stitched image is clear and accurate.

[0060] S3, combined with OpenCV tools, develops a real-time processing system. Through GPU acceleration to optimize performance, it achieves lossless coverage of panoramic monitoring of scrap steel piles and real-time identification of the landing points of scrap steel piles.

[0061] In this step, a real-time processing system is developed using OpenCV tools. Performance is optimized through GPU acceleration to achieve lossless coverage of the entire scrap metal pile for monitoring and to identify the landing points of the scrap metal in real time. The real-time processing system includes the following key components:

[0062] Image acquisition: Responsible for acquiring image data from various cameras in real time and transmitting the image data to the processing system.

[0063] Preprocessing: The acquired images are preprocessed, including grayscale conversion, binarization, edge detection, and other operations, to enhance the image's feature information.

[0064] Feature extraction: Machine learning algorithms are used to extract feature information of the scrap steel, including its type, size, weight, etc. Deep learning algorithms such as convolutional neural networks (CNNs) can be used for feature extraction.

[0065] Anomaly Detection: Based on machine learning algorithms, it automatically learns and identifies the feature patterns of anomalous data, enabling rapid and accurate detection of anomalies. The anomaly detection mechanism can identify and eliminate anomalous data caused by equipment malfunctions or environmental interference, ensuring the accuracy of the fusion results.

[0066] Real-time monitoring: Optimized performance through GPU acceleration enables real-time monitoring of the entire scrap steel pile and real-time identification of the scrap steel's landing point. The real-time monitoring module can respond quickly, ensuring real-time monitoring of the scrap steel's landing point during the unloading process.

[0067] S4 monitors the scrap steel's falling point in real time via cameras during the unloading process, and conducts quality inspection. When foreign objects or abnormalities are detected, on-site signal lights remind the crane operator to conduct a secondary on-site confirmation, thus achieving full-process quality inspection tracking and control.

[0068] In this step, the falling point of the scrap steel is monitored in real time using a camera for quality inspection, including:

[0069] Collect image data of the scrap steel drop points;

[0070] The acquired image data is preprocessed, including grayscale conversion, binarization, and edge detection;

[0071] Machine learning algorithms are used to analyze the preprocessed image data and extract the feature information of scrap steel, including the type, size and weight of the scrap steel;

[0072] Based on the extracted feature information, analysis results are generated, and the quality of scrap steel is determined according to preset thresholds and rules to determine whether it meets the standards.

[0073] If the analysis results indicate the presence of foreign objects or abnormalities in the scrap steel, record the relevant information and trigger the alarm mechanism.

[0074] By using on-site signal lights to remind the crane operator to conduct a secondary on-site confirmation, the entire quality inspection process can be tracked and controlled, specifically including:

[0075] The analysis results and alarm information are fed back to the crane operator via on-site signal lights or terminal equipment; the feedback information includes operating instructions, safety tips, and information on the type, size, and weight of the scrap steel.

[0076] Based on feedback from the crane operator, the system records the results of the secondary confirmation and updates the scrap steel quality inspection record; if an anomaly is found during the secondary confirmation, the relevant information is sent to the control center.

[0077] Another optional embodiment of this application is given below:

[0078] This application discloses a method for tracking the unloading of scrap steel inventory and identifying the landing point of scrap steel piles. Within the scrap steel warehouse, different scrap steel piles are divided. Multiple cameras (without pixel limitations; this solution uses 8-megapixel high-definition cameras) are installed around the scrap steel piles. The cameras are installed at a height of approximately 8-10 meters to ensure that the area covered by the cameras can clearly see the scrap steel, thus providing comprehensive coverage of the scrap steel pile location. Multiple cameras are installed at different locations within each scrap steel pile area to capture the entire pile.

[0079] The subsequent server stitches together the overlapping areas of all cameras to achieve seamless stitching, forming a complete, lossless image covering the scrap steel pile. First, camera calibration (intrinsic and extrinsic parameter calibration) is performed. The area captured by each camera is a fan-shaped region. At the on-site installation location, angle considerations must be taken into account. Then, feature point detection and matching (such as SIFT / SURF) are used to calculate the transformation matrix between images. Finally, multi-band fusion and other stitching algorithms are employed to eliminate seams. During this process, issues such as illumination consistency, parallax effects, and dynamic scenes may need to be addressed. A real-time processing system is developed using tools such as OpenCV, and performance is optimized through GPU acceleration, ultimately achieving lossless panoramic monitoring of the scrap steel pile.

[0080] To create a seamless, comprehensive image covering the scrap steel pile, multiple cameras are stitched together over overlapping areas. This involves calibrating the cameras (intrinsic and extrinsic parameters), with each camera capturing a fan-shaped area. The installation location and angles must be considered. Feature point detection and matching (such as SIFT / SURF) are used to calculate the transformation matrix between images. Finally, multi-band fusion and other stitching algorithms are employed to eliminate seams. This process may involve addressing issues such as lighting consistency, parallax effects, and dynamic scenes. A real-time processing system is developed using tools like OpenCV, and GPU acceleration is used to optimize performance, ultimately achieving seamless panoramic monitoring of the scrap steel pile.

[0081] When a new scrap steel truck is unloaded, the entire quality inspection system is activated. This system monitors, tracks, and identifies the scrap grabber or unloading equipment in real time. After the scrap steel is unloaded from the truck, it is grabbed and placed into the pile. As the unloading equipment enters the monitoring range of the entire scrap steel pile, the system tracks its movement and direction in real time, following its placement of the scrap steel. After the scrap steel is placed in the pile, photos are taken of the scrap steel at the unloading point. For newly added or changed scrap steel pile locations, the system identifies the dust levels at the drop point and whether the scrap steel contains any sealed components or other non-compliant scrap steel. The entire process is simultaneously and in real-time transmitted to the lobby monitoring platform and the overhead crane operator's terminal.

[0082] Two fixed unloading points are set up at the scrap steel yard. Equipment connected to the site receives a start signal to conduct remote intelligent quality inspection. If the system encounters foreign objects or other abnormalities during quality inspection, it will alert the crane operator via on-site signal lights, requiring secondary on-site confirmation. The on-site cameras installed at the scrap steel pile can monitor the scrap steel pile without blind spots, and the real-time detection data is integrated into the entire quality inspection process.

[0083] In summary, the technical effects of this application include:

[0084] 1. Conduct more comprehensive sampling of scrap steel;

[0085] 2. The lack of dust detection during the overall scrap steel quality inspection process, as no large amount of dust is generated during the grabbing process; the overall inspection process is not perfect, and it is impossible to conduct quality inspection on the already grabbed scrap steel.

[0086] 3. Quality inspection inside the vehicle may result in some items being missed.

[0087] 4. Full-process quality inspection tracking and control.

[0088] In summary, this application aims to monitor scrap steel yards and stockpiles and provide real-time video display.

[0089] This application aims to stitch together all the cameras to create a real-time view of the entire material yard.

[0090] This application optimizes the quality inspection process for scrap steel by capturing a portion of the samples.

[0091] This application applies to material yards that store scrap metal stockpiles, but are not limited to them.

[0092] This application addresses the quality inspection process, tracking, and real-time monitoring of the location and route of unloading equipment.

[0093] Please refer to Figure 2The diagram illustrates a block diagram of a scrap steel inventory unloading tracking and scrap steel pile landing point identification system provided in an embodiment of this application. The system may include:

[0094] The camera module is used to divide the scrap steel storage area into different scrap steel pile areas and install multiple cameras in different locations in each area. The installation of multiple cameras in different locations in each area includes calibrating the installed cameras, including internal and external parameter calibration, to ensure that the images captured by each camera can accurately reflect the actual location and status of the scrap steel pile.

[0095] The stitching module is used to stitch together images captured by all cameras to form a complete, lossless image covering the entire scrap steel pile. Specifically, it includes calculating the transformation matrix between images through feature point detection and matching, and eliminating seams using a multi-band fusion stitching algorithm.

[0096] The recognition module is used to develop a real-time processing system in conjunction with OpenCV tools. It optimizes performance through GPU acceleration, achieves non-destructive coverage of panoramic monitoring of scrap steel piles, and performs real-time recognition of the landing points of scrap steel piles.

[0097] The feedback module is used to monitor the falling point of scrap steel in real time through cameras during the scrap steel unloading process and to conduct quality inspection. When foreign objects or abnormalities are detected, the on-site signal lights remind the crane operator to conduct a secondary on-site confirmation, realizing full-process quality inspection tracking and control.

[0098] Specific limitations regarding the scrap steel inventory unloading tracking and scrap steel pile landing point identification system can be found in the limitations of the scrap steel inventory unloading tracking and scrap steel pile landing point identification method described above, and will not be repeated here. Each module in the aforementioned scrap steel inventory unloading tracking and scrap steel pile landing point identification system can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in or independent of the processor in a computer device in hardware form, or stored in the memory of a computer device in software form, so that the processor can call and execute the corresponding operations of each module.

[0099] In one embodiment, an electronic device is provided, which may be a computer, and its internal structure diagram may be as follows: Figure 3As shown, the electronic device includes a processor, memory, and a network interface connected via a system bus. The processor provides computing and control capabilities. The memory includes a non-volatile storage medium and internal memory. The non-volatile storage medium stores an operating system, computer programs, and a database. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage medium. The database of the computer device is used for tracking scrap steel inventory unloading and identifying scrap steel pile landing points. The network interface of the computer device is used for communication with external terminals via a network connection. When the computer program is executed by the processor, it implements a method for tracking scrap steel inventory unloading and identifying scrap steel pile landing points.

[0100] Those skilled in the art will understand that, Figure 3 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0101] In one embodiment of this application, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, it implements the steps of the above-described method for tracking the unloading of scrap steel inventory and identifying the landing point of scrap steel piles.

[0102] In one embodiment of this application, a computer program product is provided, including a computer program / instructions, which, when executed by a processor, implements the steps of the above-described method for tracking the unloading of scrap steel inventory and identifying the landing point of scrap steel piles.

[0103] The computer-readable storage medium and computer program product provided in this embodiment are similar in implementation principle and technical effect to the above method embodiments, and will not be repeated here.

[0104] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium. When executed, the computer program can include the processes of the embodiments of the above methods. Any references to memory, storage, databases, or other media used in the embodiments provided in this application can include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in M ​​forms, such as Static RAM (SRAM), Dynamic RAM (DRAM), Synchronous DRAM (SDRAM), Double Data Rate SDRAM (DDRSDRAM), Enhanced SDRAM (ESDRAM), Synchronous Link DRAM (SLDRAM), Memory Bus Direct RAM (RDRAM), Direct Memory Bus Dynamic RAM (DRDRAM), and Memory Bus Dynamic RAM (RDRAM), etc.

[0105] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0106] The embodiments described above are merely illustrative of several implementation methods of this application, and while the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the patent application. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these all fall within the protection scope of this application. Therefore, the protection scope of this patent application should be determined by the appended claims.

Claims

1. A method for tracking the unloading of scrap steel inventory and identifying the landing point of scrap steel piles, characterized in that, The method includes: The scrap steel warehouse is divided into different scrap steel pile areas, and multiple cameras are installed in different locations in each area. The installation of multiple cameras in different locations in each area includes calibrating the installed cameras, including internal and external parameter calibration, to ensure that the images captured by each camera can accurately reflect the actual location and status of the scrap steel pile. Images captured by all cameras are stitched together to form a complete, lossless image covering the entire scrap steel pile. Specifically, this involves calculating the transformation matrix between images through feature point detection and matching, and using a multi-band fusion stitching algorithm to eliminate seams. A real-time processing system was developed using OpenCV tools, and its performance was optimized through GPU acceleration to achieve lossless coverage of panoramic monitoring of scrap steel piles and to identify the landing points of scrap steel piles in real time. During the unloading of scrap steel, the falling point of the scrap steel is monitored in real time by cameras for quality inspection. When foreign objects or abnormalities are detected, the crane operator is reminded by on-site signal lights to conduct a secondary on-site confirmation, thus realizing full-process quality inspection tracking and control.

2. The method according to claim 1, characterized in that, Multiple cameras are installed at different locations in each area. The cameras include at least a high-definition camera, an infrared camera, and a thermal imaging camera. Specifically, the high-definition camera is an 8-megapixel high-definition camera, and the installation height is set to 8m-10m.

3. The method according to claim 1, characterized in that, The images captured by all the cameras are stitched together to form a complete, undamaged image covering the entire scrap metal pile, specifically including: The captured image is preprocessed; wherein the preprocessing operation includes at least grayscale conversion, binarization, and edge detection. The preprocessed image data is fused using either a weighted average method or a Bayesian fusion method, with different weights assigned based on the reliability and importance of each data type. Among these methods, a multi-band fusion algorithm is employed to improve the quality and stability of the stitched image by fusing images across different frequency bands.

4. The method according to claim 1, characterized in that, A real-time processing system was developed using OpenCV tools, including setting up an anomaly detection mechanism to identify and remove abnormal data caused by equipment failure or environmental interference. The anomaly detection mechanism is based on machine learning algorithms and can automatically learn and identify the feature patterns of abnormal data, thereby achieving fast and accurate detection of abnormal data. The real-time processing system also includes multi-threaded and multi-task processing, which can process image data from multiple cameras simultaneously, improving the system's real-time performance and response speed.

5. The method according to claim 1, characterized in that, The quality inspection is carried out by monitoring the falling point of scrap steel in real time through cameras, including: Collect image data of the scrap steel drop points; The acquired image data is preprocessed, including grayscale conversion, binarization, and edge detection; Machine learning algorithms are used to analyze the preprocessed image data and extract the feature information of scrap steel, including the type, size and weight of the scrap steel; Based on the extracted feature information, analysis results are generated, and the quality of scrap steel is determined according to preset thresholds and rules to determine whether it meets the standards. If the analysis results indicate the presence of foreign objects or abnormalities in the scrap steel, record the relevant information and trigger the alarm mechanism.

6. The method according to claim 1, characterized in that, By using on-site signal lights to remind the crane operator to conduct a secondary on-site confirmation, the entire quality inspection process can be tracked and controlled, specifically including: The analysis results and alarm information are fed back to the crane operator via on-site signal lights or terminal equipment; the feedback information includes operating instructions, safety tips, and information on the type, size, and weight of the scrap steel. Based on feedback from the crane operator, the system records the results of the secondary confirmation and updates the scrap steel quality inspection record; if an anomaly is found during the secondary confirmation, the relevant information is sent to the control center.

7. A system for tracking the unloading of scrap steel inventory and identifying the landing point of scrap steel stockpiles, characterized in that, The system includes: The camera module is used to divide the scrap steel storage area into different scrap steel pile areas and install multiple cameras in different locations in each area. The installation of multiple cameras in different locations in each area includes calibrating the installed cameras, including internal and external parameter calibration, to ensure that the images captured by each camera can accurately reflect the actual location and status of the scrap steel pile. The stitching module is used to stitch together images captured by all cameras to form a complete, lossless image covering the entire scrap steel pile. Specifically, it includes calculating the transformation matrix between images through feature point detection and matching, and eliminating seams using a multi-band fusion stitching algorithm. The recognition module is used to develop a real-time processing system in conjunction with OpenCV tools. It optimizes performance through GPU acceleration, achieves non-destructive coverage of panoramic monitoring of scrap steel piles, and performs real-time recognition of the landing points of scrap steel piles. The feedback module is used to monitor the falling point of scrap steel in real time through cameras during the scrap steel unloading process and to conduct quality inspection. When foreign objects or abnormalities are detected, the on-site signal lights remind the crane operator to conduct a secondary on-site confirmation, realizing full-process quality inspection tracking and control.

8. An electronic device, characterized in that, It includes a memory and a processor, the memory storing a computer program that, when executed by the processor, implements the method as described in any one of claims 1 to 6.

9. A computer-readable storage medium, characterized in that, It stores a computer program that, when executed by a processor, implements the method as described in any one of claims 1 to 6.

10. A computer program product comprising a computer program / instructions, characterized in that, When the computer program / instructions are executed by the processor, they implement the method described in any one of claims 1 to 6.

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