YOLOv5 object detection algorithm with visible-infrared feature interaction and fusion
XIE Yu-min
ZHANG Lang-wen
YU Xiao-yuan
XIE Wei
Abstract：Object detection is the key technology of the autonomous driving system,but object detection algorithms based on RGB often perform poorly in scenarios such as nighttime and severe weather.Therefore,the object detection algorithms fusing visible and infrared information have begun to receive a lot of research attention.However,the existing methods usually have complex fusion structures and ignore the importance of information exchange between modalities.In this paper,we take YOLOv5 as the basic framework,and propose an object detection algorithm with visible-infrared feature interaction and fusion.It uses a new backbone network,CSPDarknet53-F,which uses a dual branch structure to extract visible and infrared features,respectively,and then reconstructs the information components and proportions of each mode through feature interaction modules to improve the information exchange between modalities so that visible and infrared features can be more fully integrated.Extensive experiments on the FLIR-aligned dataset and the M3FD dataset show that the CSPDarknet53-F used in our algorithm is more excellent in terms of synergistically utilizing visible and infrared information,which improves the detection accuracy of the model and has robustness against sudden changes in light intensity.
Keywords：visible imagesinfrared imagesfeature fusioninteractionYOLOv5
Publication Date：2024-05-28
Online Publishing Date：2025-08-15(First online date of this platform, not the publication date of the document)
Pages：9( 914-922 )