1. Faculty of Land Resource Engineering, Kunming University of Science and Technology, Kunming 650093, China 2. Surveying and Mapping Geo-Informatics Technology Research Center on Plateau Mountains of Yunnan Higher Education, Kunming 650093, China 3. Key Laboratory of Geospatial Information Integration Innovation for Smart Mines, Kunming 650093, China 4. Spatial Information Integration Technology of Natural Resources in Universities of Yunnan Province, Kunming 650211, China 5. The First Geodetic Surveying Brigade of Ministry of Natural Resources, Xi’an 710054, China
As a typical and important ground target, the bridge is the vital passage between transportation lines, so automatic detection of a bridge is of great social and economic significance. Deep learning has become a new way of bridge detection, but the detection accuracy for bridges obscured by cloud and mist is low. In order to solve this problem, an automatic bridge target detection method combining Random erase (RE) data enhancement and the YOLOv4 model is proposed: firstly, the scale range of the target in the data set is determined, and the candidate frame size is obtained by K-means clustering; secondly, the cloud obscuration is simulated by a combination of RE and mosaic data enhancement; thirdly, the enhanced data set is trained by YOLOv4 network; and finally, the mean Average Precision (mAP) is used to evaluate the experimental results. The experimental results show that the detection accuracy obtained by mAP is 97.06%, which is 2.99% higher than that of traditional YOLOv4, and the average detection accuracy of bridges obscured by a cloud is improved by 12%, which verifies the effectiveness and practicability of the proposed method.
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