Improved algorithm for small target detection in remote sensing images based on YOLOv5s

Chaoyue Sun; Yajun Chen; Xiangjun Hou

doi:10.1117/12.3004660

9 October 2023 Improved algorithm for small target detection in remote sensing images based on YOLOv5s

Chaoyue Sun, Yajun Chen, Xiangjun Hou

Proceedings Volume 12791, Third International Conference on Advanced Algorithms and Neural Networks (AANN 2023); 127910E (2023) https://doi.org/10.1117/12.3004660
Event: Third International Conference on Advanced Algorithms and Neural Networks (AANN 2023), 2023, Qingdao, SD, China

Abstract

In response to the challenges of small targets and complex backgrounds in remote sensing image detection, this paper proposes an improved algorithm based on Yolov5s, named Yolov5s-RSD. The algorithm replaces the original downsampling method with SPD-Conv to reduce information loss during downsampling, adds a detection head for small targets to fully utilize their features, and introduces Biformer attention to address complex background issues. Testing on the VisDrone datasets shows that the improved algorithm achieves a 10.0% increase in map@0.5 and a 7.0% increase in map@0.5:0.95 compared to the original algorithm.

(2023) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Chaoyue Sun, Yajun Chen, and Xiangjun Hou "Improved algorithm for small target detection in remote sensing images based on YOLOv5s", Proc. SPIE 12791, Third International Conference on Advanced Algorithms and Neural Networks (AANN 2023), 127910E (9 October 2023); https://doi.org/10.1117/12.3004660

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KEYWORDS

Detection and tracking algorithms

Object detection

Remote sensing

Small targets

Target detection

Head

Algorithm development

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