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Related Experiment Video

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Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

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Published on: December 15, 2023

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 summary is machine-generated.

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.

Keywords:

image dehazing intelligent traffic surveillance low-light enhancement object detection

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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.