CW-DETR: An Efficient Detection Transformer for Traffic Signs in Complex Weather
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View abstract on PubMed
Summary
This summary is machine-generated.This study introduces CW-DETR, a novel framework for robust traffic sign detection in complex weather conditions. It significantly improves accuracy and maintains efficiency for intelligent transportation systems.
Area Of Science
- Computer Vision
- Artificial Intelligence
- Intelligent Transportation Systems
Background
- Adverse weather conditions like rain, fog, and snow degrade traffic sign features, challenging existing detection systems.
- Real-time traffic sign detection is crucial for intelligent transportation systems (ITS).
Purpose Of The Study
- To develop an end-to-end detection framework, CW-DETR (Complex Weather Detection Transformer), for robust traffic sign detection under adverse weather.
- To address weather-induced feature deterioration and improve real-time detection performance.
Main Methods
- The CW-DETR framework builds upon RT-DETR, integrating four key innovations: multipath feature enhancement network (FPFENet), Multiscale Edge Enhancement Module (MEEM), adaptive dual-stream bidirectional feature pyramid network (ADBF-FPN), and multiscale convolutional gating module (MCGM).
- These modules are designed to preserve textures, enhance boundaries, compensate for cross-scale features, and reduce semantic-spatial confusion.
Main Results
- CW-DETR achieved 69.0% AP and 94.4% AP50 on the CCTSDB2021 dataset, outperforming state-of-the-art real-time detectors by 2.3-5.7 percentage points.
- The model demonstrated computational efficiency with 56.8 GFLOPs.
- Cross-dataset evaluations on TT100K, TSRD, CNTSSS, and LNTU-TSD confirmed the model's robust generalization capabilities.
Conclusions
- CW-DETR offers an effective solution for all-weather traffic sign detection.
- The proposed framework significantly enhances detection performance in challenging weather conditions.
- CW-DETR demonstrates strong generalization, making it suitable for real-world intelligent transportation applications.
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