面向水域人员的不安全行为识别算法与应用

Unsafe behavior recognition algorithm and application for water personnel

为保障水域人员生命安全,针对水域环境人员不安全行为识别问题,提出一种基于Faster-RCNN+改进SlowFast的水域人员行为识别网络。主要工作包括以下几点:第一:根据采集的95段视频制作水域人员动作行为数据集,其中共有161687张快通道帧,5543张关键帧,共计标注框9173个。第二:使用Faster-RCNN+SlowFast模块化设计对水域人员动作行为数据集进行目标定位及行为分析,其实验结果表现良好,可以识别水域人员位置及行为。第三:提出一种与时域压缩策略相结合方式的改进SlowFast网络,结果表明改进SlowFast网络模型识别准确率提高5.0%,推理速度得到约1.14倍提升,实验证明可有效帮助识别水域人员的行为识别问题。

In order to ensure the life safety of water personnel,a behavior recognition network based on Faster-RCNN+SlowFast improved was proposed for unsafe behavior recognition of water personnel.The main work includes the following points:First,according to the 95 videos collected,the dataset of water area personnel's action behavior is constructed,in which there are 161687 fast channel frames,5543 key frames and a total of 9173 labeling frames.Secondly,the Faster-RCNN+SlowFast modular design is used to conduct target location and behavior analysis on the dataset of personnel's action behavior in water area.The experimental results show good performance and can identify the location and behavior of personnel in water area.Thirdly,an improved SlowFast network combined with time-domain compression strategy is proposed.The results show that the recognition accuracy of the improved SlowFast network model increases by 5.0%,and the inference speed increases by about 1.14 times.The experiment proves that the improved network model can effectively help to identify the behavior recognition problms of people in water areas.