Abstract: In order to solve the problem that the current mainstream low-light pedestrian detection framework has poor performance due to insufficient image brightness and contrast in this task, this paper proposes the RetinaHA-YOLOv8 algorithm. The algorithm uses RetinexFormer as a pre-processing module to restore the damaged image, ensuring that the subsequent algorithm  can extract clearer and more useful features from the enhanced image. Additionally, it uses the hybrid attention transformation (HAT) attention mechanism to retain key information in the initial stage and promote deep fusion after feature fusion. Finally, in order to balance the additional computational burden and meet the real-time detection requirements, the online re-parameterized convolution technology is introduced to improve the inference speed and frames per second while maintaining the detection accuracy. The experimental results verify the effectiveness of the RetinaHA-YOLOv8 algorithm on the public low-light pedestrian detection dataset, with AP increased by 5.4%, 11.7% and 9.5% respectively, while meeting the real-time requirements in practical applications. 

Key words: low-light pedestrian detection, RetinexFormer framework, HAT attention mechanism, online reparameterized convolution