Bright Channel Prior Attention for Multispectral Pedestrian Detection
Chenhang Cui Jinyu Xie Yechenhao Yang
Multispectral methods have gained considerable attention due to their
promising performance across various fields. However, most existing methods
cannot effectively utilize information from two modalities while optimizing
time efficiency. These methods often prioritize accuracy or time efficiency,
leaving room for improvement in their performance. To this end, we propose a
new method bright channel prior attention for enhancing pedestrian detection in
low-light conditions by integrating image enhancement and detection within a
unified framework. The method uses the V-channel of the HSV image of the
thermal image as an attention map to trigger the unsupervised auto-encoder for
visible light images, which gradually emphasizes pedestrian features across
layers. Moreover, we utilize unsupervised bright channel prior algorithms to
address light compensation in low light images. The proposed method includes a
self-attention enhancement module and a detection module, which work together
to improve object detection. An initial illumination map is estimated using the
BCP, guiding the learning of the self-attention map from the enhancement
network to obtain more informative representation focused on pedestrians. The
extensive experiments show effectiveness of the proposed method is demonstrated
through.