Enhancing Road Safety: Real-Time Classification of Low Visibility Foggy Weather Using ABNet Deep-Learning Model
Publication: Journal of Transportation Engineering, Part A: Systems
Volume 150, Issue 10
Abstract
Frequent highway accidents occur in the Guizhou region, among which poor visibility due to fog is one of the main causative factors. In this region, traditional large-scale, high-computational-power fog monitoring systems are difficult to install and have high costs due to complex terrains, high altitudes, and winding roads, causing traffic management departments to fail to obtain fog information accurately and timely, which undoubtedly becomes a significant safety hazard. To solve this problem, this study proposes a fog monitoring solution based on the lightweight deep learning model ABNet. The solution first preprocesses the input images, including generating the fog concentration distribution map using the fog imaging model, and obtaining the high-frequency component image using filters based on the 2D discrete wavelet transform. Subsequently, these two processed images and the original image are fed into the three branches of the ABNet for training to fully extract fog concentration and high frequency information, thereby improving model performance and prediction accuracy. The ABNet model parameters only require 38.52MB, and the computational complexity is a mere 1.71GFLOPs, effectively solving the limited storage and computational resources problem in edge computing. The model was evaluated using the Guizhou highway fog weather data set, and ABNet exhibited impressive performance with a composite classification accuracy as high as 92.3%, reaching 92.4% in average precision rate and 92.3% in average recall rate. In comparison, the performance of models like VisNet, VGG16, EfficientNetV2, and Swin Transformer V2 seemed inferior. The experimental results validated the excellent performance of the ABNet model in terms of accuracy and efficiency. The ABNet model in this study, with its lightweight deep learning design, small parameter scale, and lower computational power requirements, provides a solution suitable for complex terrains and practical environments of edge computing devices, and it provides vital technological support to improve traffic safety on the highways in the Guizhou region.
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Data Availability Statement
Some or all data, models, or code generated or used during the study are proprietary or confidential in nature and may only be provided with restrictions.
Acknowledgments
This research received financial support from the Science and Technology Program of the Department of Education of Hubei Province (Grant No. D20221604) and the Key Research and Development Program of Hubei Province (Grant No. 2021BBA235).
Author contributions: conceptualization, Cao Yuan and Lin Li; methodology, Cao Yuan, Lin Li and Dongdong Xiong; software, Lin Li and Dongdong Xiong; validation, Lin Li, Dongdong Xiong, and Cao Yuan; formal analysis, Yaqin Li, Jing Hu, and Hao Li; data curation, Lin Li, Dongdong Xiong, and Xiaoling Xia; writing–original draft preparation, Cao Yuan and Lin Li; writing–review and editing, Cao Yuan, Lin Li, and Dongdong Xiong; and funding acquisition, Cuihua Zuo. All authors have read and agreed to the published version of the manuscript.
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Information & Authors
Information
Published In
Journal of Transportation Engineering, Part A: Systems
Volume 150 • Issue 10 • October 2024
Copyright
© 2024 American Society of Civil Engineers.
History
Received: Dec 29, 2023
Accepted: May 10, 2024
Published online: Aug 2, 2024
Published in print: Oct 1, 2024
Discussion open until: Jan 2, 2025
ASCE Technical Topics:
- Accidents
- Artificial intelligence (AI)
- Artificial intelligence and machine learning
- Business management
- Climates
- Computer models
- Computer programming
- Computer vision and image processing
- Computing in civil engineering
- Engineering fundamentals
- Environmental engineering
- Infrastructure
- Meteorology
- Methodology (by type)
- Model accuracy
- Models (by type)
- Neural networks
- Practice and Profession
- Public administration
- Public health and safety
- Safety
- Traffic accidents
- Traffic engineering
- Traffic management
- Traffic safety
- Transportation engineering
- Weather conditions
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