Multi-Modal Pedestrian Detection Algorithm Based on Illumination Perception Weight Fusion

Existing pedestrian target detection algorithm based on visible light and infrared modal fusion has a high missed detection rate in all-weather environment. In this paper, we propose a novel multi-modal pedestrian target detection algorithm based on illumination perception weight fusion to solve this problem. First, ResNet50, incorporating an efficient channel attention (ECA) mechanism module, was used as a feature extraction network to extract the features of both visible light and infrared modes, respectively. Second, the existing illumination weighted sensing fusion strategy was improved. A new illumination weighted sensing fusion mechanism was designed to attain the corresponding weights of the visible light and infrared modes, and weighted fusion was performed to achieve fusion features to reduce the missed detection rate of the algorithm. Finally, the multi-modal features extracted from the last layer of the feature network and the generated fusion features were fed into the detection network to accomplish the detection of pedestrian targets. Experimental results show that the proposed algorithm has an excellent detection performance on the KAIST dataset, and the missed detection rate for pedestrian targets in all-weather is 11.16%.