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A novel XM-YOLOViT real-time detection algorithm for pedestrians and vehicles in foggy days based on YOLOV5 framework is proposed, which effectively solves the problems of dense target interference and obscuration by haze, and improves the detection effect in complex foggy environments. 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 of verified on four datasets with different foggy environments, respectively. The experimental results show that the accuracy, recall, and mAP of the XM-YOLOViT model are respectively 54.95%, 41.93%, and 43.15%, with an F1-Score of 0.474, and the accuracy improved by 3.42%, recall improved by 7.08%, mAP improved by 7.52%, and F1 score improved by 13.94% compared to the baseline model, while model parameters are decreased by 41.7% to 4.09M, FLOPs of 25.2G, and detection speed of 70.93 FPS. The XM-YOLOViT model performs better 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 UAV foggy weather detection tasks, especially for extremely small targets. Our source code is available at: https://github.com/AFeiV8/XM-YOLOViT.

Research on real-time detection algorithm for pedestrian and vehicle in foggy weather based on lightweight XM-YOLOViT