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The maintenance and management of navigation marks are essential for ensuring the safety of transportation on the Yangtze River. Considering the current inspection and management approaches, this paper introduces an intelligent method for inspecting inland river navigation marks using Unmanned Aerial Vehicles (UAVs). The method enables real-time monitoring of navigation marks using UAV video inspection. A UAV data acquisition platform captures video images of these marks. We have developed the ED-YOLOv5s object detection algorithm to detect and classify navigation marks. Building on this, the system can automatically assess the light quality and status of navigation marks at night. The ED-YOLOv5s algorithm is an enhancement of the YOLOv5s model, incorporating the ECA mechanism and DFFN structure, which are based on the ResNet principle. This modification enhances the model’s capability for network feature fusion. Experimental results indicate improvements in navigation mark detection with the ED-YOLOv5s. Although precision decreased by 1.76% when compared to the YOLOv5s model, recall and mAP@0.5 increased by 3.59% and 2.97%, respectively. The detection results for light quality state from video images of navigation marks at night accurately reflect actual conditions. We have developed an intelligent sensing scheme for navigation marks on the Yangtze River based on the improved model. This scheme has been implemented in the Yichang section of the Yangtze River, significantly reducing the cost of daily inspections, enhancing cruise monitoring effectiveness, facilitating intelligent maintenance decisions for navigation marks, and further ensuring the navigational safety of the Yangtze River.

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Research on Object Detection for Intelligent Sensing of Navigation Mark in Yangtze River

  • 会议论文

  • 发表日期：

    2025年

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  • Taotao He,
  • Pinfu Yang,
  • Xiaofeng Zou,
  • Shengli Zhang,
  • Shuqing Cao,
  • Chaohua Gan

摘要

The maintenance and management of navigation marks are essential for ensuring the safety of transportation on the Yangtze River. Considering the current inspection and management approaches, this paper introduces an intelligent method for inspecting inland river navigation marks using Unmanned Aerial Vehicles (UAVs). The method enables real-time monitoring of navigation marks using UAV video inspection. A UAV data acquisition platform captures video images of these marks. We have developed the ED-YOLOv5s object detection algorithm to detect and classify navigation marks. Building on this, the system can automatically assess the light quality and status of navigation marks at night. The ED-YOLOv5s algorithm is an enhancement of the YOLOv5s model, incorporating the ECA mechanism and DFFN structure, which are based on the ResNet principle. This modification enhances the model’s capability for network feature fusion. Experimental results indicate improvements in navigation mark detection with the ED-YOLOv5s. Although precision decreased by 1.76% when compared to the YOLOv5s model, recall and mAP@0.5 increased by 3.59% and 2.97%, respectively. The detection results for light quality state from video images of navigation marks at night accurately reflect actual conditions. We have developed an intelligent sensing scheme for navigation marks on the Yangtze River based on the improved model. This scheme has been implemented in the Yichang section of the Yangtze River, significantly reducing the cost of daily inspections, enhancing cruise monitoring effectiveness, facilitating intelligent maintenance decisions for navigation marks, and further ensuring the navigational safety of the Yangtze River.