Application of YOLOv8 in monocular downward multiple Car Target detection

TL;DR

This paper enhances YOLOv8 for monocular downward multiple car target detection, improving accuracy and efficiency in autonomous driving scenarios by integrating advanced network structures and detection pipelines.

Contribution

It introduces structural reparameterization, a bidirectional pyramid network, and a novel detection pipeline into YOLOv8, enabling better detection of multi-scale and small objects.

Findings

Detection accuracy of 65% for multi-scale objects

Effective detection of small and remote objects

Significant improvements over traditional methods

Abstract

Autonomous driving technology is progressively transforming traditional car driving methods, marking a significant milestone in modern transportation. Object detection serves as a cornerstone of autonomous systems, playing a vital role in enhancing driving safety, enabling autonomous functionality, improving traffic efficiency, and facilitating effective emergency responses. However, current technologies such as radar for environmental perception, cameras for road perception, and vehicle sensor networks face notable challenges, including high costs, vulnerability to weather and lighting conditions, and limited resolution.To address these limitations, this paper presents an improved autonomous target detection network based on YOLOv8. By integrating structural reparameterization technology, a bidirectional pyramid structure network model, and a novel detection pipeline into the YOLOv8 framework, the proposed approach achieves highly efficient and precise detection of multi-scale, small, and remote objects. Experimental results demonstrate that the enhanced model can effectively detect both large and small objects with a detection accuracy of 65%, showcasing significant advancements over traditional methods.This improved model holds substantial potential for real-world applications and is well-suited for autonomous driving competitions, such as the Formula Student Autonomous China (FSAC), particularly excelling in scenarios involving single-target and small-object detection.