Multi-scale dense object detection in remote sensing images based on improved YOLOv5

Yuan Liu; Fuqiang Chen

doi:10.1117/12.3031604

18 July 2024 Multi-scale dense object detection in remote sensing images based on improved YOLOv5

Yuan Liu, Fuqiang Chen

Proceedings Volume 13179, International Conference on Optics and Machine Vision (ICOMV 2024); 131790O (2024) https://doi.org/10.1117/12.3031604
Event: International Conference on Optics and Machine Vision (ICOMV 2024), 2024, Nanchang, China

Abstract

To address the challenges of significant variations in target scales and high computational complexity, low accuracy and slow inference speed in target detection of remote sensing images, this study proposes a multi-scale dense target detection network for remote sensing images based on improved YOLOv5. Firstly, the network adopts partial convolution in feature extraction to improve the inference speed for remote sensing images. Secondly, the ASPPF module is introduced to address the problem of multi-scale feature information loss during feature fusion in the pyramid network. Finally, the WISE-IOU function is introduced to compute IOULOSS, which reduces the negative impact of dense arrangement on the accuracy of horizontal box detection. Experimental evaluations on the DOTAv1.0 dataset show that the improved model achieves mAP0.5 of 0.73, demonstrating improvements in parameters, FLOPs, FPS, and other aspects compared to YOLOv5.

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

Citation Download Citation

Yuan Liu and Fuqiang Chen "Multi-scale dense object detection in remote sensing images based on improved YOLOv5", Proc. SPIE 13179, International Conference on Optics and Machine Vision (ICOMV 2024), 131790O (18 July 2024); https://doi.org/10.1117/12.3031604

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