Bright Channel Prior Attention for Multispectral Pedestrian Detection

TL;DR

This paper introduces a novel bright channel prior attention method that enhances multispectral pedestrian detection in low-light conditions by integrating image enhancement and detection in a unified framework, improving feature focus and performance.

Contribution

The paper proposes a new attention mechanism combining unsupervised bright channel prior algorithms with a self-attention module for improved low-light pedestrian detection.

Findings

Effective in low-light pedestrian detection scenarios

Improves feature emphasis on pedestrians

Demonstrates superior performance in experiments

Abstract

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.