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Machine vision algorithms can be applied in many fields, including but not limited to human bone key point detection, hands off the steering wheel, road garbage detection, mixing tank steering recognition, FCT equipment final inspection, spill and drip recognition, cargo type recognition, chip number and QR code recognition, traffic sign recognition, roadside detection, vehicle wash detection, and license plate detection and recognition. The following briefly describes the basic principles and implementation methods of these applications.
Human bone key point detection: This application mainly utilizes deep learning and computer vision technology to analyze human images or videos, automatically detecting and recognizing human bone key points, such as head, neck, shoulder, elbow, wrist, hip, knee, ankle, and other parts, for posture estimation, behavior analysis, and other aspects.
Hands off the steering wheel: This application monitors the driver's hands off the steering wheel in real-time through the camera to determine whether the driver is correctly controlling the vehicle. This usually requires the use of image processing and computer vision techniques, such as background subtraction, object detection, etc., to achieve both hand detection and recognition.
Road garbage detection: This application mainly uses machine vision technology to analyze road images or videos, automatically detecting and identifying road garbage, such as plastic bags, paper, etc., for road maintenance and management.
Mixing tank turning recognition: This application automatically recognizes the turning direction of the mixing tank through image processing and computer vision technology, thereby achieving automatic control of the mixing process.
FCT equipment final inspection: This application typically uses machine vision technology to inspect products and ensure product quality.
Spill and Drip Identification: This application uses image processing and computer vision technology to automatically detect and recognize the phenomenon of spills and drips in items, in order to achieve automated control of the production process.
Cargo type recognition: This application mainly uses machine vision technology to analyze cargo images or videos, automatically detect and identify the type of cargo, and is used for logistics management and transportation.
Chip number and QR code recognition: This application automatically recognizes chip number and QR code through image processing and computer vision technology, thereby achieving product traceability and management.
Traffic sign recognition: This application mainly uses machine vision technology to automatically detect and recognize road traffic signs, including speed limit signs, no parking signs, etc., for vehicle navigation and autonomous driving.
Road edge detection: This application uses image processing and computer vision technology to automatically detect the position of road edges or edges, used for autonomous driving and vehicle navigation.
Vehicle flushing detection: This application mainly utilizes machine vision technology to automatically detect the vehicle flushing process to ensure the quality of the flushing process.
License plate detection and recognition: This application uses image processing and computer vision technology to automatically detect and recognize license plate numbers, achieving functions such as vehicle management and traffic monitoring.
Black smoke vehicle recognition: This application uses image processing and computer vision technology to automatically detect the smoke emitted from the rear of the vehicle, in order to determine whether the vehicle is a black smoke vehicle, and is used for environmental monitoring and management.
These applications require the processing and analysis of images or videos, using techniques such as image processing, computer vision, and machine learning. The application of these technologies can greatly improve production efficiency, product quality, traffic safety, and other aspects.
Machine vision algorithms can be applied in many fields, including but not limited to human bone key point detection, hands off the steering wheel, road garbage detection, mixing tank steering recognition, FCT equipment final inspection, spill and drip recognition, cargo type recognition, chip number and QR code recognition, traffic sign recognition, roadside detection, vehicle wash detection, and license plate detection and recognition. The following briefly describes the basic principles and implementation methods of these applications.
Human bone key point detection: This application mainly utilizes deep learning and computer vision technology to analyze human images or videos, automatically detecting and recognizing human bone key points, such as head, neck, shoulder, elbow, wrist, hip, knee, ankle, and other parts, for posture estimation, behavior analysis, and other aspects.
Hands off the steering wheel: This application monitors the driver's hands off the steering wheel in real-time through the camera to determine whether the driver is correctly controlling the vehicle. This usually requires the use of image processing and computer vision techniques, such as background subtraction, object detection, etc., to achieve both hand detection and recognition.
Road garbage detection: This application mainly uses machine vision technology to analyze road images or videos, automatically detecting and identifying road garbage, such as plastic bags, paper, etc., for road maintenance and management.
Mixing tank turning recognition: This application automatically recognizes the turning direction of the mixing tank through image processing and computer vision technology, thereby achieving automatic control of the mixing process.
FCT equipment final inspection: This application typically uses machine vision technology to inspect products and ensure product quality.
Spill and Drip Identification: This application uses image processing and computer vision technology to automatically detect and recognize the phenomenon of spills and drips in items, in order to achieve automated control of the production process.
Cargo type recognition: This application mainly uses machine vision technology to analyze cargo images or videos, automatically detect and identify the type of cargo, and is used for logistics management and transportation.
Chip number and QR code recognition: This application automatically recognizes chip number and QR code through image processing and computer vision technology, thereby achieving product traceability and management.
Traffic sign recognition: This application mainly uses machine vision technology to automatically detect and recognize road traffic signs, including speed limit signs, no parking signs, etc., for vehicle navigation and autonomous driving.
Road edge detection: This application uses image processing and computer vision technology to automatically detect the position of road edges or edges, used for autonomous driving and vehicle navigation.
Vehicle flushing detection: This application mainly utilizes machine vision technology to automatically detect the vehicle flushing process to ensure the quality of the flushing process.
License plate detection and recognition: This application uses image processing and computer vision technology to automatically detect and recognize license plate numbers, achieving functions such as vehicle management and traffic monitoring.
Black smoke vehicle recognition: This application uses image processing and computer vision technology to automatically detect the smoke emitted from the rear of the vehicle, in order to determine whether the vehicle is a black smoke vehicle, and is used for environmental monitoring and management.
These applications require the processing and analysis of images or videos, using techniques such as image processing, computer vision, and machine learning. The application of these technologies can greatly improve production efficiency, product quality, traffic safety, and other aspects.
