Research on Real-Time Detection Algorithm for Pedestrian and Vehicle in Foggy Weather Based on Lightweight XM-YOLOViT

A novel XM-YOLOViT real-time detection algorithm for pedestrians and vehicles in foggy weather based on YOLOV5 framework is proposed, which effectively solves the problems of dense target interference and obscuration by haze, and the detection effect in complex foggy environments is improved. Firstly, Inverted Residual Block and MobileViTV3 Block are introduced to construct XM-net feature extraction network, secondly, EIOU is used as a location loss function and a high-resolution detection layer is added in the Neck region. In terms of data, a nebulization method is designed to map images from fogless space to foggy space based on the atmospheric scattering model and the dark channel prior. Finally, the validity on four datasets under different foggy environments is verified, respectively. The experimental results show that the accuracy, recall and mAP of the XM-YOLOViT model are 54.95%, 41.93% and 43.15% respectively, and with an F1-Score of 0.474, which is 3.42%,7.08%,7.52% and 13.94% improved, the model parameter reduction of 41.7% to 4.09M, the FLOPs is 25.2G and detection speed is 70.93 FPS compared to the baseline model. The XM-YOLOViT model has better performance than the advanced YOLO detectors, the F1-Score and mAP are improved by 5.57% and 3.65% compared with YOLOv7-tiny, and 2.38%, 2.37% respectively compared with YOLOv8s. Therefore, the XM-YOLOViT algorithm proposed in this article has high detection accuracy and an extremely lightweight structure, which can effectively improve the efficiency and quality of detection tasks for UAV in foggy weather, especially for extremely small targets. Our source code is available at:
https://github.com/AFeiV8/XM-YOLOViT
.