Student behavior detection algorithm based on RI-YOLO
To address the issues of low accuracy in student behavior detection algorithms,which often lead to missed detections and false positives,the article proposes an improved student behavior detection algorithm based on YOLOv5s called RI-YOLO.The algorithm optimizes the C3 module by introducing Receptive-Field Attention Convolution(RFAConv),proposing a new RFAC3 module that can more accurately capture subtle local features,thereby enhancing the network's feature extraction capabilities.Additionally,it adopts an Inner-IoU loss function based on auxiliary bounding boxes to replace traditional loss functions,accelerating the convergence speed of the model.Testing on the student classroom behavior dataset SCB-Dataset3 shows that RI-YOLO improves mean average precision(mAP50)by 1.5%compared to YOLOv5,and mAP50:95 by 1.2%,demonstrating superior detection performance when compared with other mainstream detection models.
object detectionstudent behaviorRFAC3auxiliary bounding boxmean average precision
万方数据
维普
