基于深度学习和物联网技术的水库危险行为识别系统

Reservoir Risky Behavior Recognition System Based on Deep Learning and Internet of Things

针对当前水库信息化程度低、监管不完善等问题,设计开发了一套基于深度学习算法和物联网技术的水库危险行为识别系统,主要包括视频监控、识别预警和预警统计等多个功能模块,构建了一个面向水库危险行为的实时全天候监控、识别、预警平台,旨在加强水库周边区域的安全管理,预防和减少因钓鱼、游泳和非法越界等危险行为导致的安全事故。为解决自然环境复杂、小目标对象检测难的问题,系统基于目标检测算法(YOLO),融合了高效多尺度注意力模型(EMA),以提高危险行为识别的准确率和可靠性,在实际部署和应用中能有效缓解水库巡查不到位的问题,全面提升了水库安全监管的智能化水平。

In order to solve the problems of low degree of informatization and imperfect supervision of reservoirs,a reservoir risk behavior recognition system based on deep learning algorithm and Internet of Things technology has been designed and developed.The system aims to strengthen the safety management of the area around the reservoir and prevent and reduce accidents caused by dangerous behaviors such as fishing,swimming and illegal border crossing.In order to solve the problem of complex natural environment and difficult detection of small target objects,the system integrates the efficient multi-scale attention model(EMA)based on the object detection algorithm(YOLO)to improve the accuracy and reliability of dangerous behavior recognition.In the actual deployment and application,the system can effectively alleviate the problem of insufficient reservoir inspection,and comprehensively improve the intelligent level of reservoir safety supervision.