Target Detection Algorithm in High-Resolution Remote Sensing Images Based on YOLOv5
With the rapid development of remote sensing technology,high-resolution remote sensing images provide strong support for urban planning,environmental monitoring and disaster assessment with their high-precision surface details.However,due to the increasing size diversity of targets and background complexity,it is challenging to accurately detect and identify specific targets in high-resolution images.YOLOv5 has demonstrated excellent performance in the field of real-time target detection,with significant advantages in processing speed and high accuracy in a variety of complex scenarios.Based on the YOLOv5 algorithm framework,an object detection algorithm for high-resolution remote sensing images is proposed in this paper.Improved multi-scale feature fusion technology is introduced to effectively improve the detection capability of the model for small objects under complex background.The results show that the proposed algorithm has excellent performance in high resolution remote sensing image target detection and has practical potential.
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