Pos:  Project >> Smart Engineering >> Content

Sand Mining Vessel Regulation


Pub Date:2025-01-08 20:24 Page Views:


With the growing need for river ecological protection and water resource management, illegal sand mining has severely impacted river environments and water conservancy safety. Monitoring sand mining vessels and their activities along rivers efficiently and accurately is a critical challenge for water conservancy regulation and law enforcement. However, traditional monitoring methods struggle with precise identification and tracking due to complex factors like lighting changes, obstructions, and uncertain target behaviors in water areas. To address this, this project proposes an intelligent sand mining vessel regulation system based on computer vision, integrating multiple key technologies for smart detection and monitoring of sand mining activities. Specifically, the project first applies object segmentation technology to accurately identify sand mining vessels from images and videos. It then uses moving target tracking technology to monitor their trajectories in real-time, analyzing their activity patterns in rivers. Additionally, human pose estimation technology extracts key points of personnel to precisely identify sand mining behaviors along the river. Furthermore, scene semantic understanding technology is used to analyze river environments and work scenes, enhancing the detection accuracy for abnormal sand mining and environmental adaptability. The project's innovations include: integrating object segmentation and moving target tracking for precise vessel identification and trajectory monitoring; using human pose estimation to improve behavioral detection accuracy; and leveraging scene semantic understanding to strengthen the system's comprehensive perception of river environments, enhancing monitoring stability in complex scenarios. The experimental results show that the proposed system achieves significant detection outcomes on multiple real-world river video datasets, with a sand mining vessel recognition accuracy of 87% and a 9% improvement in personnel sand mining behavior detection accuracy. This research offers efficient and intelligent technical support for water conservancy regulation, with wide application prospects in river monitoring, illegal sand mining control, and ecological protection.