Multi-scene infrared image processing based on fusion algorithm

Shuwei Wang; Youyou Xi; Jinbao Yang; Xiaojie Tong; Chen Yang

doi:10.1117/12.3014302

30 April 2024 Multi-scene infrared image processing based on fusion algorithm

Shuwei Wang, Youyou Xi, Jinbao Yang, Xiaojie Tong, Chen Yang

Proceedings Volume 13156, Sixth Conference on Frontiers in Optical Imaging and Technology: Imaging Detection and Target Recognition; 131560A (2024) https://doi.org/10.1117/12.3014302
Event: Sixth Conference on Frontiers in Optical Imaging Technology and Applications (FOI2023), 2023, Nanjing, JS, China

Abstract

Infrared imaging technology is widely used in military and civilian fields, but in practical applications, accurate and effective detection and tracking of infrared small targets is a bottleneck problem that needs to be solved urgently. In response to the problem that traditional algorithms are difficult to handle complex scenes with low signal-to-noise ratio and deep learning algorithms rely heavily on data, the proposed algorithm combines traditional algorithms with deep learning algorithms and is applied to detect and track infrared moving targets in various complex scenes, with resolutions ranging from 640 * 512 to 320 * 256 video sequences. At the same time, traditional algorithms include both single frame and multi frame detection methods. In order to avoid the problem of poor real-time performance, we selected the TMS320C6678 hardware platform and implemented simulation applications using a DSP+FPGA architecture. Experimental results have shown that this algorithm has excellent performance in object detection and tracking.

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

Citation Download Citation

Shuwei Wang, Youyou Xi, Jinbao Yang, Xiaojie Tong, and Chen Yang "Multi-scene infrared image processing based on fusion algorithm", Proc. SPIE 13156, Sixth Conference on Frontiers in Optical Imaging and Technology: Imaging Detection and Target Recognition, 131560A (30 April 2024); https://doi.org/10.1117/12.3014302

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