Infrared dim target detection technology based on IRI-CNN

Jia-Kai He; De-Zhen Yang; Cheng-Bin An; Jiang-Yong Li; Cheng-Zhang Huang

doi:10.1117/12.2617526

15 February 2022 Infrared dim target detection technology based on IRI-CNN

Jia-Kai He, De-Zhen Yang, Cheng-Bin An, Jiang-Yong Li, Cheng-Zhang Huang

Proceedings Volume 12166, Seventh Asia Pacific Conference on Optics Manufacture and 2021 International Forum of Young Scientists on Advanced Optical Manufacturing (APCOM and YSAOM 2021); 121665E (2022) https://doi.org/10.1117/12.2617526
Event: Seventh Asia Pacific Conference on Optics Manufacture and 2021 International Forum of Young Scientists on Advanced Optical Manufacturing (APCOM and YSAOM 2021), 2021, Hong Kong, Hong Kong

Abstract

Due to airborne infrared detection device under the limit of detection range will cause the target to be detected in the image of pixels smaller, less radiation, easy to drown in the background. Meanwhile high speed and dim target in complex scene change, however, traditional algorithm only by artificial convolution kernel parameters for object detection and segmentation threshold, will cause more false alarm. To solve this problem, a lightweight dim target detection method based on CNN neural network architecture is proposed in this paper, which effectively improves the detection rate of dim target and reduces the false alarm rate. Through simulation comparison and statistics, it is verified that the detection rate of this algorithm can reach 93%~98% in different scenes, and the average number of false alarms is 0.1~2.6 in a single frame, which realizes the low false alarm detection of targets.

Citation Download Citation

Jia-Kai He, De-Zhen Yang, Cheng-Bin An, Jiang-Yong Li, and Cheng-Zhang Huang "Infrared dim target detection technology based on IRI-CNN", Proc. SPIE 12166, Seventh Asia Pacific Conference on Optics Manufacture and 2021 International Forum of Young Scientists on Advanced Optical Manufacturing (APCOM and YSAOM 2021), 121665E (15 February 2022); https://doi.org/10.1117/12.2617526

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KEYWORDS

Target detection

Detection and tracking algorithms

Infrared detectors

Image enhancement

Neural networks

Infrared imaging

Submerged target detection

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