[1]YING Yujie,DING Fei,YU Yaoyuan,et al.M-YOLO: a lightweight multi-scale feature fusion-based model for adverse weather object detection[J].CAAI transactions on intelligent systems,2026,21(5):1292-1299.[doi:10.11992/tis.202601011]
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M-YOLO: a lightweight multi-scale feature fusion-based model for adverse weather object detection

CAAI transactions on intelligent systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 21 Number of periods: 2026 5 Page number: 1292-1299 Column: 学术论文—智能系统 Public date: 2026-09-05
Title:
M-YOLO: a lightweight multi-scale feature fusion-based model for adverse weather object detection
Author(s):
YING Yujie1; 2;  DING Fei1; 2;  YU Yaoyuan1; 2;  ZENG Yu1; 2;  JI Yun1; 2
1. Jiangsu Key Laboratory of Broadband Wireless Communications and Internet of Things, Nanjing University of Posts and Telecommunications, Nanjing 210003, China;
2. School of Internet of Things, Nanjing University of Posts and Telecommunications, Nanjing 210003, China
Keywords:
YOLOv5; target detection; multi-access edge computing; attention mechanism; intelligent transportation; image enhancement; lightweight model; adverse weather
CLC:
TP391.41
DOI:
10.11992/tis.202601011
Abstract:
To improve the traffic target recognition accuracy of intelligent transportation systems in complex meteorological environments, an enhanced YOLOv5 model (M-YOLO) based on multi-access edge computing (MEC) architecture is proposed. A two-stage image enhancement mechanism was designed to realize the fusion of fog/rain/snow multi-source meteorological features; a channel and spatial synergistic Convolutional Block Attention Mechanism (CBAM) was introduced to enhance the model’s target recognition and localization ability in complex backgrounds; a P2/4 small-target detection layer was added based on the YOLOv5 architecture to improve the detection accuracy of small-sized targets; meanwhile, M-YOLO also adopted the MPDIoU loss function to optimize the bounding box regression. Finally, experimental validation was performed with Foggy-Cityscapes, Rain-Cityscapes, and Snow-Cityscapes datasets, and the results show that M-YOLO exhibits better average detection accuracy. Under mixed weather conditions, the mAP50 of the M-YOLO model reaches 0.485, which represents a significant improvement over the benchmark model (0.415), providing an efficient solution for real-time traffic monitoring in edge computing environments.
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