  The significance of background information is frequently overlooked in
contemporary research concerning 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 development 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 integration 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.
