基于深度学习的翻越行为检测

Crossing Behavior Detection Based on Deep Learning

翻越行为检测对疫情管控、社会治安等有着重要意义,一定程度上可以减少因为违规翻越行为而造成的意外事故.针对目前翻越行为检测任务实时性差、需要先验知识的问题,本文将Faster RCNN+SlowFast时空行为检测算法应用在翻越行为检测任务中,将翻越行为进行拆分检测.为提高时空行为检测算法中目标的检测精度和速率将目标检测模块Faster RCNN改为实时性高且轻量化的YOLOv5;其次针对同一行为不同视角下广泛的类内多样性的问题,将Fast支路和Slow支路的residual block分别改为AC residual block和SE residual block来加强网络对关键特征与细粒度特征的学习能力,最后设计翻越行为检测算法进行攀爬与下降两种状态的连续性检测,实验结果显示该网络平均准确率达93.5%,在翻越行为检测中表现出良好的性能.

Crossing behavior detection is of great significance for epidemic control and social security and can reduce accidents caused by illegal crossing behavior to a certain extent.In view of the problems of poor real-time performance and the need for prior knowledge in the current crossing behavior detection task,this study applies the Faster RCNN+SlowFast spatiotemporal behavior detection algorithm to the crossing behavior detection task to split and detect the crossing behavior.In order to improve the detection accuracy and speed of the target in the spatiotemporal behavior detection algorithm,the target detection module,namely Faster RCNN is changed to lightweight YOLOv5 with high realtime performance.Then,according to the extensive in-class diversity under different perspectives of the same behavior,the residual block of the Fast branch and Slow branch is changed to AC residual block and SE residual block,respectively,so as to strengthen the network’s learning ability to key features and fine-grained features.Finally,the crossing behavior detection algorithm is designed to detect the continuity of climbing and descending states.Experimental results show that the average accuracy of the network reaches 93.5%,which shows excellent performance in crossing behavior detection.