Student behavior detection based on YOLOv4-Bi

With the continuous development of the times, intelligent teaching assistance is also attracting more and more attention in education. The intelligent detection algorithm for student behavior is gradually becoming more precise. In university classrooms, students sleep on mobile phones and are more serious. Intelligent education can more accurately identify student behaviors to help teachers optimize teaching methods, thereby improving students' classroom learning effects. This paper studies and improves YOLOv4, and proposes a network structure called YOLOv4-Bi, which mainly adds the enhanced feature extraction network of YOLOv4 to the feature extraction structure of jumping and top-down, bottom-up combined paths. The used student classroom recording video is enhanced by taking the frame data and training, and testing on this data set. The original YOLOv4 is compared with the network of the improved PANet module and the Faster R-CNN network, and the data is carried out in the data set. It is verified that the mAP of the improved YOLOv4 network is higher than the mAP of the original unimproved YOLOv4 network. Compared with the original network, YOLOv4 is more suitable for student detection and recognition.