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A Lightweight Multi-Stage Visual Detection Approach for Complex Traffic Scenes
Xuanyi Zhao1, Xiaohan Dou1, Jihong Zheng2
1School of Electronic Information and Electrical Engineering, Yangtze University, Jingzhou 434023, China.
Sensors (Basel, Switzerland)
|August 28, 2025
Summary
This study introduces a robust visual detection framework for complex traffic scenes, enhancing images degraded by haze and low light. The system improves vehicle and pedestrian detection accuracy, offering a practical solution for intelligent transportation systems.
Area of Science:
- Computer Vision
- Artificial Intelligence
- Intelligent Transportation Systems
Background:
- Image degradation in traffic scenes (haze, low illumination, occlusion) hinders object detection performance.
- Existing systems struggle with robustly identifying vehicles and pedestrians under adverse conditions.
Purpose of the Study:
- To develop a robust visual detection framework for complex traffic environments.
- To enhance object detection accuracy for vehicles and pedestrians despite image degradation.
Main Methods:
- Multi-stage image enhancement using ConvIR, CIDNet, and a novel Horizontal/Vertical-Intensity color space strategy.
- A lightweight detection architecture, Mamba-Driven Lightweight Detection Network with RT-DETR Decoding, incorporating VSSBlock, XSSBlock, and VisionClueMerge modules.
Main Results:
- The proposed method achieved a 1.0 percentage point increase in mAP@50-90 over YOLOv12s on traffic surveillance datasets (0.759 to 0.769).
- Demonstrated superior deployment adaptability and robustness with reduced parameter complexity and computational overhead.
Conclusions:
- The framework provides an effective solution for object detection in challenging traffic conditions.
- The integration of advanced image enhancement and lightweight detection architecture improves system performance and efficiency.
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