RCSLFNet: a novel real-time pedestrian detection network based on re-parameterized convolution and channel-spatial location fusion attention for low-resolution infrared image

Abstract

A novel real-time infrared pedestrian detection algorithm is introduced in this study. The proposed approach leverages re-parameterized convolution and channel-spatial location fusion attention to tackle the difficulties presented by low-resolution, partial occlusion, and environmental interference in infrared pedestrian images. These factors have historically hindered the accurate detection of pedestrians using traditional algorithms. First, to tackle the problem of weak feature representation of infrared pedestrian targets caused by low resolution and partial occlusion, a new attention module that integrates channel and spatial is devised and introduced to CSPDarkNet53 to design a new backbone CSLF-DarkNet53. The designed attention model can enhance the feature expression ability of pedestrian targets and make pedestrian targets more prominent in complex backgrounds. Second, to enhance the efficiency of detection and accelerate convergence, a multi-branch decoupled detector head is designed to operate the classification and location of infrared pedestrians separately. Finally, to improve poor real-time without losing precision, we introduce the re-parameterized convolution (Repconv) using parameter identity transformation to decouple the training process and detection process. During the training procedure, to enhance the fitting ability of small convolution kernels, a multi-branch structure with convolution kernels of different scales is designed. Compared with the nice classical detection algorithms, the results of the experiment show that the proposed RCSLFNet not only detects partial occlusion infrared pedestrians in complex environments accurately but also has better real-time performance on the KAIST dataset. The mAP@0.5 reaches 86% and the detection time is 0.0081 s, 2.9% higher than the baseline.