Improved YOLOv5 for object detection in UAV aerial images

Hongxin Zhao; Yunzheng Zhang; Xiaoyang Hu

doi:10.1117/12.3025446

28 February 2024 Improved YOLOv5 for object detection in UAV aerial images

Hongxin Zhao, Yunzheng Zhang, Xiaoyang Hu

Proceedings Volume 13071, International Conference on Mechatronic Engineering and Artificial Intelligence (MEAI 2023); 130712J (2024) https://doi.org/10.1117/12.3025446
Event: International Conference on Mechatronic Engineering and Artificial Intelligence (MEAI 2023), 2023, Shenyang, China

Abstract

UAV aerial image object detection is of great significance for intelligent target identification and tracking, but the target under the UAV viewpoint is subject to large changes in target scale due to the influence of light, and there are cases of occlusion, low target resolution, etc., which lead to low model detection accuracy, misdetection, leakage and other problems. To address the above problems, an improved object detection method for UAV aerial images is proposed based on the YOLOv5. The method introduces Space-to-depth Convolution (SPD-Conv), Normalization-based Attention Module (NAM) and regression loss function, and conducts a large number of experiments on Visdrone2019 dataset. The experimental results show that the improved YOLOv5 algorithm improves the mean accuracy percentage (mAP) by 6.1% and the mAP@0.5:0.95 by 5%.

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

Citation Download Citation

Hongxin Zhao, Yunzheng Zhang, and Xiaoyang Hu "Improved YOLOv5 for object detection in UAV aerial images", Proc. SPIE 13071, International Conference on Mechatronic Engineering and Artificial Intelligence (MEAI 2023), 130712J (28 February 2024); https://doi.org/10.1117/12.3025446

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