Abnormal behavior identification of examinees based on improved YOLOv5

JianFeng Wen; YiHai Qin; Shan Hu

doi:10.1117/12.2674630

23 May 2023 Abnormal behavior identification of examinees based on improved YOLOv5

JianFeng Wen, YiHai Qin, Shan Hu

Proceedings Volume 12604, International Conference on Computer Graphics, Artificial Intelligence, and Data Processing (ICCAID 2022); 126043O (2023) https://doi.org/10.1117/12.2674630
Event: 2nd International Conference on Computer Graphics, Artificial Intelligence, and Data Processing (ICCAID 2022), 2022, Guangzhou, China

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.

Citation Download Citation

JianFeng Wen, YiHai Qin, and Shan Hu "Abnormal behavior identification of examinees based on improved YOLOv5", Proc. SPIE 12604, International Conference on Computer Graphics, Artificial Intelligence, and Data Processing (ICCAID 2022), 126043O (23 May 2023); https://doi.org/10.1117/12.2674630

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