Examination is a way to select talents, and a perfect invigilation strategy can improve the fairness of the examination. To realize the automatic detection of abnormal behavior in the examination room, the method based on the improved YOLOv3 (The third version of the You Only Look Once algorithm) algorithm is proposed. The YOLOv3 algorithm is improved by using the K-Means algorithm, GIoUloss, focal loss, and Darknet32. In addition, the frame-alternate dual-thread method is used to optimize the detection process. The research results show that the improved YOLOv3 algorithm can improve both the detection accuracy and detection speed. The frame-alternate dual-thread method can greatly increase the detection speed. The mean Average Precision (mAP) of the improved YOLOv3 algorithm on the test set reached 88.53%, and the detection speed reached 42 Frames Per Second (FPS) in the frame-alternate dual-thread detection method. The research results provide a certain reference for automated invigilation.

中文翻译：

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基于改进型YOLOv3的异常行为检测

考试是选拔人才的一种方法，完善的监考策略可以提高考试的公平性。为了实现对检查室异常行为的自动检测，提出了一种基于改进的YOLOv3算法的方法。通过使用K-Means算法GIoUloss对YOLOv3算法进行了改进，焦点损失和Darknet32。另外，使用帧交替双线程方法来优化检测过程。研究结果表明，改进的YOLOv3算法可以提高检测精度和检测速度。帧交替双线程方法可以大大提高检测速度。改进的YOLOv3算法在测试集上的平均平均精度（mAP）达到88.53％，并且在帧交替双线程检测方法中，检测速度达到了每秒42帧（FPS）。研究结果为自动监考提供了一定的参考。