Improved YOLOv5-based object detection model for UAV inspection images

Yifeng Sun; Minyan Xia; Yu Jin; Haonan Chen; Yanyan Huang

doi:10.1117/12.3035163

19 July 2024 Improved YOLOv5-based object detection model for UAV inspection images

Yifeng Sun, Minyan Xia, Yu Jin, Haonan Chen, Yanyan Huang

Proceedings Volume 13213, International Conference on Image Processing and Artificial Intelligence (ICIPAl 2024); 132132P (2024) https://doi.org/10.1117/12.3035163
Event: International Conference on Image Processing and Artificial Intelligence (ICIPAl2024), 2024, Suzhou, China

Abstract

In the field of Unmanned Aerial Vehicle (UAV) patrol image detection, the challenge of accurately and swiftly detecting images is a significant issue. However, current detection models often fail to achieve a balance between precision and detection speed. To address this problem, we propose a detection model named Uni-YOLOv5s. Our model introduces a module called UniReplkNet into the YOLOv5s model, which utilizes large convolutions to extract image feature information. We evaluated our model on the InsPlAD dataset. The results demonstrate that our model can rapidly detect UAV patrol images and achieve superior performance metrics. The recall rate is 92.7%, which is 2.5% higher than the baseline YOLOv5s, Thus verifying that our method is superior to existing methods.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Yifeng Sun, Minyan Xia, Yu Jin, Haonan Chen, and Yanyan Huang "Improved YOLOv5-based object detection model for UAV inspection images", Proc. SPIE 13213, International Conference on Image Processing and Artificial Intelligence (ICIPAl 2024), 132132P (19 July 2024); https://doi.org/10.1117/12.3035163

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