SimAM-based optimization algorithm for small target detection

Jing-yi Qu; Yun-long Li; Hao-hao Li; Shan-liang Liu

doi:10.1117/12.3005083

9 October 2023 SimAM-based optimization algorithm for small target detection

Jing-yi Qu, Yun-long Li, Hao-hao Li, Shan-liang Liu

Proceedings Volume 12791, Third International Conference on Advanced Algorithms and Neural Networks (AANN 2023); 127910Q (2023) https://doi.org/10.1117/12.3005083
Event: Third International Conference on Advanced Algorithms and Neural Networks (AANN 2023), 2023, Qingdao, SD, China

Abstract

With the development of deep learning, small target detection application scenarios are gradually becoming more widespread, and higher demands are made on small target detection accuracy. The attention mechanism is a simple and convenient way to improve target detection accuracy. In order to improve the detection performance of the attention mechanism for small targets, a SimAM-based attention method for small target detection is proposed by us, which changes the activation method of SimAM and add hyperparameters to improve the detection effect of small targets. The experimental results show that the algorithm has good detection performance.

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

Citation Download Citation

Jing-yi Qu, Yun-long Li, Hao-hao Li, and Shan-liang Liu "SimAM-based optimization algorithm for small target detection", Proc. SPIE 12791, Third International Conference on Advanced Algorithms and Neural Networks (AANN 2023), 127910Q (9 October 2023); https://doi.org/10.1117/12.3005083

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