Object Detection in Autonomous Driving: Advances in Computer Vision Techniques

With the rapid development of autonomous driving technology, target detection, as the core link of its environmental perception, has become a research hotspot in the field of computer vision This article systematically reviews the application status, key challenges, and future trends of computer vision-based object detection technology in autonomous driving. This article provides a comprehensive review of computer vision-based object detection technology in autonomous driving scenarios. It discusses the importance of object detection in autonomous driving, analyzes key challenges such as adapting to complex environments and balancing real-time performance with accuracy, and summarizes current solutions like context learning and algorithm optimization. The paper also highlights future trends, including the development of lightweight deep learning models and the application of computer vision in special environments such as urban logistics and mining areas.

Publication Details

Journal
Springer Link (Chiba Institute of Technology)
Published
2025-09-08
DOI
https://doi.org/10.1051/itmconf/20257803017/pdf
Primary Topic
Geochemistry and Geologic Mapping
Type
article
Field-Weighted Citation Impact
0.00
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article

Object Detection in Autonomous Driving: Advances in Computer Vision Techniques

Springer Link (Chiba Institute of Technology)
Geochemistry and Geologic Mapping
article

Object Detection in Autonomous Driving: Advances in Computer Vision Techniques

article en

Abstract

With the rapid development of autonomous driving technology, target detection, as the core link of its environmental perception, has become a research hotspot in the field of computer vision This article systematically reviews the application status, key challenges, and future trends of computer vision-based object detection technology in autonomous driving. This article provides a comprehensive review of computer vision-based object detection technology in autonomous driving scenarios. It discusses the importance of object detection in autonomous driving, analyzes key challenges such as adapting to complex environments and balancing real-time performance with accuracy, and summarizes current solutions like context learning and algorithm optimization. The paper also highlights future trends, including the development of lightweight deep learning models and the application of computer vision in special environments such as urban logistics and mining areas.

Springer Link (Chiba Institute of Technology)
Sustainable cities and communities
Openalex Percentile: Top 44%
Geochemistry and Geologic Mapping
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