Remote sensing images target detection based on deep learning

Yuan Zhang; Lingran Zhao; Linjing Jia; Yuhao Zhang; Hongquan Qu

doi:10.1117/12.2615300

15 October 2021 Remote sensing images target detection based on deep learning

Yuan Zhang, Lingran Zhao, Linjing Jia, Yuhao Zhang, Hongquan Qu

Proceedings Volume 11933, 2021 International Conference on Neural Networks, Information and Communication Engineering; 119330X (2021) https://doi.org/10.1117/12.2615300
Event: 2021 International Conference on Neural Networks, Information and Communication Engineering, 2021, Qingdao, China

Abstract

Target detection based on deep learning methods has important applications in the field of remote sensing images target detection. Aiming at the disadvantages of the small target scale for remote sensing images, this paper proposes an improved YOLOv4 algorithm to improve the detection effect of remote sensing images. The experimental results show that the algorithm proposed in this paper has an average accuracy rate of 2.25% higher than that of the original YOLOv4 algorithm. The accuracy of detecting small-scale targets has been significantly improved, and the amount of model parameters has also been reduced compared to the original algorithm.

Citation Download Citation

Yuan Zhang, Lingran Zhao, Linjing Jia, Yuhao Zhang, and Hongquan Qu "Remote sensing images target detection based on deep learning", Proc. SPIE 11933, 2021 International Conference on Neural Networks, Information and Communication Engineering, 119330X (15 October 2021); https://doi.org/10.1117/12.2615300

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