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Related Experiment Video

Updated: Jun 24, 2025

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

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Published on: December 15, 2023

Research on multi-object detection technology for road scenes based on SDG-YOLOv5.

Zhenyang Lv¹, Rugang Wang¹, Yuanyuan Wang¹

¹School of Information Technology, Yancheng Institute of Technology, Yancheng, China.

Peerj. Computer Science

|June 10, 2024

Summary

This study introduces SDG-YOLOv5, an enhanced algorithm for road scene detection, improving accuracy and real-time performance. The new method effectively addresses challenges in detecting small objects and enhances bounding box regression.

Keywords:

Attention mechanism Decoupled detection head Intelligent driving Road scene detection

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Area of Science:

Computer Vision
Artificial Intelligence
Machine Learning

Background:

Road scene detection faces challenges with low accuracy and real-time performance.
Existing algorithms struggle with accurate bounding box regression and small target detection.

Purpose of the Study:

To enhance road scene detection accuracy and real-time performance.
To improve bounding box regression and small target detection capabilities.

Main Methods:

Introduced SDG-YOLOv5 algorithm incorporating SIoU Loss function for accurate angle prediction.
Employed lightweight decoupled heads (DHs) to separate classification and regression tasks.
Utilized Global Attention Mechanism Group Convolution (GAMGC) for enhanced contextual information processing.

Main Results:

SDG-YOLOv5 achieved improvements in mAP@.5 of 2.2% (Udacity), 3.4% (BDD100K), and 1.0% (KITTI) over original YOLOv5.
The algorithm demonstrated a detection speed of 30.3 FPS.
Significant improvements in both detection accuracy and real-time performance were observed.

Conclusions:

SDG-YOLOv5 effectively addresses the limitations of existing road scene detection algorithms.
The enhanced algorithm offers a robust solution for accurate and efficient real-time road scene analysis.
The proposed methods contribute to advancements in autonomous driving perception systems.