Transformer-based vehicle detection algorithm under foggy conditions

Yuan Li; Xiangqian Liu

doi:10.1117/12.3054457

3 January 2025 Transformer-based vehicle detection algorithm under foggy conditions

Yuan Li, Xiangqian Liu

Author Affiliations +

Proceedings Volume 13442, Fifth International Conference on Signal Processing and Computer Science (SPCS 2024); 134420R (2025) https://doi.org/10.1117/12.3054457
Event: Fifth International Conference on Signal Processing and Computer Science (SPCS 2024), 2024, Kaifeng, China

Abstract

An efficient foggy weather vehicle recognition framework is proposed to address the challenges of vehicle identification under adverse weather conditions in this study. Enhanced high-resolution data is used as input, and an image restoration model is employed to effectively capture multi-scale feature information. Additionally, an image dehazing model is incorporated to restore clear vehicle features from foggy and blurred images, thereby improving the accuracy of vehicle detection in foggy conditions. In the experimental analysis, visualized results are first presented, and the model's performance is analyzed by comparing it with the detection results of other algorithms. The data is further analyzed, with several dehazing algorithms integrated into the vehicle detection model for ablation experiments to validate the model's effectiveness.

(2025) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Yuan Li and Xiangqian Liu "Transformer-based vehicle detection algorithm under foggy conditions", Proc. SPIE 13442, Fifth International Conference on Signal Processing and Computer Science (SPCS 2024), 134420R (3 January 2025); https://doi.org/10.1117/12.3054457

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KEYWORDS

Object detection

Detection and tracking algorithms

Transformers

Image enhancement

Data modeling

Image processing

Image restoration

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