Detection and recognition of hand abnormal state based on deep learning algorithm

The system collects and detects hand information of relevant persons in a specific area, and transmits the data to the server for calculation and processing. In response to the abnormal state of the hand, the result picture is fed back to the staff in real time through the mobile app. This paper proposes a method for detecting and recognizing abnormal hand states based on the improved yolov3 algorithm. The system collects real-time pictures of the hand through the camera to determine whether the hand is carrying ring, bandages, and whether there are bleeding points. After optimizing the network and preprocessing the data, the algorithm accuracy can reach 99.7%. In addition, the simplified processing of the model can reduce the burden on the hardware system.