Research on remote sensing target detection algorithm based on improved YOLOv5

Zhongqiang Wang; Feng Liu

doi:10.1117/12.3029334

22 May 2024 Research on remote sensing target detection algorithm based on improved YOLOv5

Zhongqiang Wang, Feng Liu

Proceedings Volume 13176, Fourth International Conference on Machine Learning and Computer Application (ICMLCA 2023); 131760K (2024) https://doi.org/10.1117/12.3029334
Event: Fourth International Conference on Machine Learning and Computer Application (ICMLCA 2023), 2023, Hangzhou, China

Abstract

Accurate and fast detection of targets in remote sensing images has always been a challenge due to the small size and diverse scales of the targets in the images. In order to improve the detection accuracy and shorten the detection time, this paper proposes a remote sensing image target detection method based on the CA attention mechanism and the lightweight network Slim-neck, which improves the target detection ability of YOLOv5s network. This paper focuses on enhancing the neck portion of the YOLOv5s. First, to improve the network's capacity for feature learning, multiple CA attention mechanisms are added to Neck. Secondly, the GSConv and VoVGSCSP modules are introduced to replace the last Conv and C3 modules in the neck. These improvements have allowed the network to increase detection speed along with detection precision. Experiments on the dataset DOTA-v1.0 show that the improved YOLOv5s model increases the detection precision by 3.5% and reduces the computational complexity by 2.5%.

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

Citation Download Citation

Zhongqiang Wang and Feng Liu "Research on remote sensing target detection algorithm based on improved YOLOv5", Proc. SPIE 13176, Fourth International Conference on Machine Learning and Computer Application (ICMLCA 2023), 131760K (22 May 2024); https://doi.org/10.1117/12.3029334

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KEYWORDS

Target detection

Remote sensing

Neck

Detection and tracking algorithms

Convolution

Data modeling

Performance modeling

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