Abnormal behavior identification of examinees based on improved YOLOv5

Abstract

Traditional examination rooms rely on invigilators to monitor examinees in real time and use cameras to help invigilate the exam, which is prone to problems such as incomplete monitoring, inadequate response to cheating. This paper builds an abnormal behavior recognition model in examination room based on LOLOv5 and the cascading attention mechanism. The model effectively improves the backbone network of YOLOv5, and combines the cascading attention mechanism to enhance the features. Finally, the model is tested on the self-created dataset. The results show that the examinees abnormal behavior detection results of the proposed model are P (92.53%), mAP (93.52%), fps (0.547). Compared with several classical abnormal behavior detection algorithms, the proposed algorithm has higher accuracy and recognition speed.