<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>At the current stage, the rapid Development of autonomous driving has made object detection in traffic scenarios a vital research task. Object detection is the most critical and challenging task in computer vision. Deep learning, with its powerful feature extraction capabilities, has found widespread applications in safety, military, and medical fields, and in recent years has expanded into the field of transportation, achieving significant breakthroughs. This survey is based on the theory of deep learning. It systematically summarizes the Development and current research status of object detection algorithms, and compare the characteristics, advantages and disadvantages of the two types of algorithms. With a focus on traffic signs, vehicle detection, and pedestrian detection, it summarizes the applications and research status of object detection in traffic scenarios, highlighting the strengths, limitations, and applicable scenarios of various methods. It introduces techniques for optimizing object detection algorithms, summarizes commonly used object detection datasets and traffic scene datasets, along with evaluation criteria, and performs comparative analysis of the performance of deep learning algorithms. Finally, it concludes the development trends of object detection algorithms in traffic scenarios, providing research directions for intelligent transportation and autonomous driving.</p>
</abstract>

At the current stage, the rapid Development of autonomous driving has made object detection in traffic scenarios a vital research task. Object detection is the most critical and challenging task in computer vision. Deep learning, with its powerful feature extraction capabilities, has found widespread applications in safety, military, and medical fields, and in recent years has expanded into the field of transportation, achieving significant breakthroughs. This survey is based on the theory of deep learning. It systematically summarizes the Development and current research status of object detection algorithms, and compare the characteristics, advantages and disadvantages of the two types of algorithms. With a focus on traffic signs, vehicle detection, and pedestrian detection, it summarizes the applications and research status of object detection in traffic scenarios, highlighting the strengths, limitations, and applicable scenarios of various methods. It introduces techniques for optimizing object detection algorithms, summarizes commonly used object detection datasets and traffic scene datasets, along with evaluation criteria, and performs comparative analysis of the performance of deep learning algorithms. Finally, it concludes the development trends of object detection algorithms in traffic scenarios, providing research directions for intelligent transportation and autonomous driving.

A Review of Object Detection in Traffic Scenes Based on Deep Learning

Department of Mechanical and Manufacturing, Faculty of Engineering

Applied Mathematics and Nonlinear Sciences

This work is licensed under the Creative Commons Attribution 4.0 International License.

{"article-title":"A Review of Object Detection in Traffic Scenes Based on Deep Learning"}

At the current stage, the rapid Development of autonomous driving has made object detection in traffic scenarios a vital research task. Object detection is the...

A Review of Object Detection in Traffic Scenes Based on Deep Learning

Data publikacji: 26 lut 2024

Zakres stron: -

Otrzymano: 02 sty 2024

Przyjęty: 11 sty 2024

DOI: https://doi.org/10.2478/amns-2024-0322

Słowa kluczowe
Object detection, Deep learning, Traffic scenarios, Autonomous driving systems

© 2024 Ruixin Zhao et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

A Review of Object Detection in Traffic Scenes Based on Deep Learning

Data publikacji: 26 lut 2024

Zakres stron: -

Otrzymano: 02 sty 2024

Przyjęty: 11 sty 2024

DOI: https://doi.org/10.2478/amns-2024-0322

Słowa kluczoweObject detection, Deep learning, Traffic scenarios, Autonomous driving systems

© 2024 Ruixin Zhao et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Słowa kluczowe
Object detection, Deep learning, Traffic scenarios, Autonomous driving systems