Journal of Infrared and Millimeter Waves, Volume. 41, Issue 6, 1092(2022)
GPNet:Lightweight infrared image target detection algorithm
A lightweight infrared image target detection algorithm GPNet is proposed to address the need for accurate and real-time target detection in resource-constrained infrared imaging systems. The feature extraction network is optimized using GhostNet, feature fusion is performed using an improved PANet, and a depth-separable convolution is used to replace the ordinary 3×3 convolution at specific locations to better extract multi-scale features and reduce the number of parameters. Experiments on public datasets show that the algorithm in this paper reduces the number of parameters by 81% and 42% compared with YOLOv4 and YOLOv5-m, respectively; the average mean accuracy is improved by 2.5% and the number of parameters is reduced by 51% compared with YOLOX-m; the number of parameters is 12.3 M and the detection time is 14 ms, which achieves a balance between detection accuracy and number of parameters.
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Xian-Guo LI, Ming-Teng CAO, Bin LI, Yi LIU, Chang-Yun MIAO. GPNet:Lightweight infrared image target detection algorithm[J]. Journal of Infrared and Millimeter Waves, 2022, 41(6): 1092
Category: Research Articles
Received: May. 25, 2022
Accepted: --
Published Online: Feb. 6, 2023
The Author Email: Xian-Guo LI (lixianguo@tiangong.edu.cn)