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.

翻译：多光谱方法因其在多个领域中的优异性能而受到广泛关注。然而，现有大多数方法在优化时间效率的同时难以有效利用两种模态的信息。这些方法往往优先考虑精度或时间效率，性能仍有提升空间。为此，我们提出一种新的亮度通道先验注意力方法，通过将图像增强与检测集成在统一框架中，增强低光照条件下的行人检测性能。该方法利用热成像HSV图像的V通道作为注意力图，触发可见光图像的无监督自编码器，从而逐层强化行人特征。此外，我们采用无监督亮度通道先验算法处理低光照图像的光照补偿。所提方法包含自注意力增强模块与检测模块，二者协同提升目标检测效果。通过BCP算法估计初始光照图，指导增强网络自注意力图的学习，从而获得更聚焦于行人的信息表征。大量实验结果表明了该方法的有效性。