An infrared object detection algorithm based on feature fusion

Ying Meng; Chao Ma; Yaoyuan Zeng; Wei An

doi:10.1117/12.2622855

1 December 2021 An infrared object detection algorithm based on feature fusion

Ying Meng, Chao Ma, Yaoyuan Zeng, Wei An

Proceedings Volume 12079, Second IYSF Academic Symposium on Artificial Intelligence and Computer Engineering; 1207903 (2021) https://doi.org/10.1117/12.2622855
Event: 2nd IYSF Academic Symposium on Artificial Intelligence and Computer Engineering, 2021, Xi'an, China

Abstract

Benefiting from the superiority visual capacity of infrared imaging system such as long operating distance and all-weather work, infrared target detection is widely applied in weapon guidance, security protection and other fields. Aiming at the characteristic of infrared targets, we propose an infrared detection algorithm based on feature fusion. Firstly, gaussian filter and background suppression are employed to improve signal-to-noise ratio and increase the contrast between target and background, respectively. Secondly, a feature fusion architecture is added to backbone for feature extraction, so as to obtain features with rich information. Finally, in order to solve the problem of imbalance between positive and negative samples, the network is trained by focal loss to improve training performance. We evaluate the proposed detection algorithm on public infrared image datasets. The results demonstrate that the proposed algorithm can achieve promising performance.

Citation Download Citation

Ying Meng, Chao Ma, Yaoyuan Zeng, and Wei An "An infrared object detection algorithm based on feature fusion", Proc. SPIE 12079, Second IYSF Academic Symposium on Artificial Intelligence and Computer Engineering, 1207903 (1 December 2021); https://doi.org/10.1117/12.2622855

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