Nighttime pedestrian detection based on Fore-Background contrast learning

The significance of background information is frequently overlooked in contemporary research con-cerning channel attention mechanisms. This study addresses the issue of suboptimal single-spectral nighttime pedestrian detection performance under low-light conditions by incorporating background information into the channel attention mechanism. Despite numerous studies focusing on the devel-opment of efficient channel attention mechanisms, the relevance of background information has been largely disregarded. By adopting a contrast learning approach, we reexamine channel attention with regard to pedestrian objects and background information for nighttime pedestrian detection, resulting in the proposed Fore-Background Contrast Attention (FBCA). FBCA possesses two primary attributes: (1) channel descriptors form remote dependencies with global spatial feature information; (2) the integra-tion of background information enhances the distinction between channels concentrating on low-light pedestrian features and those focusing on background information. Consequently, the acquired channel descriptors exhibit a higher semantic level and spatial accuracy. Experimental outcomes demonstrate that FBCA significantly outperforms existing methods in single-spectral nighttime pedestrian detection, achieving state-of-the-art results on the NightOwls and TJU-DHD-pedestrian datasets. Furthermore, this methodology also yields performance improvements for the multispectral LLVIP dataset. These findings indicate that integrating background information into the channel attention mechanism effectively mitigates detector performance degradation caused by illumination factors in nighttime scenarios.& COPY; 2023 Elsevier B.V. All rights reserved.

Automated summary

This study addresses the issue of suboptimal single-spectral nighttime pedestrian detection performance by incorporating background information into the channel attention mechanism. By adopting a contrast learning approach, the proposed Fore-Background Contrast Attention (FBCA) method significantly outperforms existing methods in nighttime pedestrian detection and achieves state-of-the-art results on multiple datasets.