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June 3, 2026

Real-time stereo vehicle detection and collision warning system based on YOLOv8 and SGBM

Key Points

The aim is to develop a real-time vehicle detection and collision warning system using stereo vision techniques.
Integrated YOLOv8n for vehicle detection and SGBM for depth estimation.
Fused detection results with disparity maps to compute 3D vehicle positions.
Evaluated on the KITTI dataset for performance metrics.
Achieved high detection accuracy and depth precision above 100 FPS.
Significantly outperformed traditional vehicle detection methods.
Provided a low-cost and efficient solution for driver assistance systems.

Abstract

This study proposes a robust, real-time stereo vision system integrating YOLOv8n for fast, accurate vehicle detection and SGBM for reliable depth estimation, addressing traditional method limitations. By fusing YOLOv8n's detections with SGBM's disparity maps, 3D vehicle positions are calculated for collision risk assessment. Trained and evaluated on KITTI, the system achieves exceptional detection accuracy and depth precision with over 100 FPS, significantly outperforming conventional approaches and offering a viable solution for low-cost, high-efficiency driver assistance.

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