UAV image object detection based on improved YOLOv5s

Juxing Di; Fangtao Feng; Yang Yang; Wencheng Zhang

doi:10.1117/12.3011970

7 December 2023 UAV image object detection based on improved YOLOv5s

Juxing Di, Fangtao Feng, Yang Yang, Wencheng Zhang

Proceedings Volume 12941, International Conference on Algorithms, High Performance Computing, and Artificial Intelligence (AHPCAI 2023); 129413J (2023) https://doi.org/10.1117/12.3011970
Event: Third International Conference on Algorithms, High Performance Computing, and Artificial Intelligence (AHPCAI 203), 2023, Yinchuan, China

Abstract

As we all know, the objects in the images taken by Unmanned Aerial Vehicle (UAV) are relatively small, while our naked eyes are able to extract the information almost instantly, even from far away, image resolution and computational resources limitations make detecting smaller objects a genuinely challenging task for machines. We propose an algorithm based on YOLOv5s with small computational resources and high accuracy, so as to be applied to edge detection devices such as unmanned aerial vehicles. By simplifying the depth of the feature extraction network and adjusting the size of the feature map of the detection head, the target in the image taken by UAV can be accurately identified. In the end, we reduced the number of parameters by 70% at the expense of a little accuracy, while improving accuracy by 15.25%, or 5.2 percentage points, over the baseline.

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

Citation Download Citation

Juxing Di, Fangtao Feng, Yang Yang, and Wencheng Zhang "UAV image object detection based on improved YOLOv5s", Proc. SPIE 12941, International Conference on Algorithms, High Performance Computing, and Artificial Intelligence (AHPCAI 2023), 129413J (7 December 2023); https://doi.org/10.1117/12.3011970

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