BiThermalNet: a lightweight network with BNN RPN for thermal object detection

Chunyu Yuan; Sos S. Agaian

doi:10.1117/12.2618104

27 May 2022 BiThermalNet: a lightweight network with BNN RPN for thermal object detection

Chunyu Yuan, Sos S. Agaian

Proceedings Volume 12100, Multimodal Image Exploitation and Learning 2022; 121000B (2022) https://doi.org/10.1117/12.2618104
Event: SPIE Defense + Commercial Sensing, 2022, Orlando, Florida, United States

Abstract

As traditional RGB cameras cannot perform well under weak light in the darkness and poor weather conditions, thermal cameras have become an essential component of edge systems. This paper proposes a lightweight, faster binarized R-CNN network (a state-of-the-art instance segmentation model), called BiThermalNet, for thermal object detection with high detection capabilities and lower memory usage. It designs a new Region Proposal Network(RPN) structure with a binary neural network (BNN) to lower model size by 16%, having higher accuracy performance. BiThermalNet adds novel-designed residual gates to maximum information entropy and offers channel-wise weight and bias to reduce errors from binarization. The extensive experiments on different thermal datasets (such as Dogs&People Thermal Dataset, UNIRI-TID) confirm that BiThermalNet can outperform traditional faster R-CNN by sizable margins with smaller models size. Moreover, a comparative analysis of the proposed methods on thermal images will also be presented.

Conference Presentation

Citation Download Citation

Chunyu Yuan and Sos S. Agaian "BiThermalNet: a lightweight network with BNN RPN for thermal object detection", Proc. SPIE 12100, Multimodal Image Exploitation and Learning 2022, 121000B (27 May 2022); https://doi.org/10.1117/12.2618104

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