基于深度学习的水利领域智能视频识别系统设计与实现OA

Traditional water conservancy video surveillance systems were limited to basic image acquisition,leading to a risk of serious accidents due to inadequate oversight.To address these issues,an intelligent video recognition system was designed and developed for on-site scenarios in water conservancy projects,including reservoirs,rivers,pumping stations,and gate stations.By utilizing an improved YOLOv5 object detection model integrated with Transformer and self-attention mechanisms,the system established a series of detection models covering water gauge reading,floating objects,road waterlogging,and personnel intrusion,enabling real-time analysis of field video streams.Developed in Java using a microservices architecture,the system comprised two core subsystems:AI-based video analysis and model training.It not only performed real-time AI recognition and automatically triggers alert notifications but also facilitated self-iterative model optimization through continuous sample enrichment,thereby improving recognition accuracy.The research results promote the intelligent upgrading of surveillance systems in the water conservancy industry and provide robust technical support for modernizing water conservancy management.