1. Object Detection Using Deep Learning Methods in Traffic Scenarios
- Author
-
Zhijun Hou and Azzedine Boukerche
- Subjects
050210 logistics & transportation ,General Computer Science ,Computer science ,business.industry ,Deep learning ,05 social sciences ,Feature extraction ,02 engineering and technology ,Machine learning ,computer.software_genre ,Convolutional neural network ,Object detection ,Field (computer science) ,Theoretical Computer Science ,Task (project management) ,Open research ,0502 economics and business ,0202 electrical engineering, electronic engineering, information engineering ,Key (cryptography) ,020201 artificial intelligence & image processing ,Artificial intelligence ,business ,computer - Abstract
The recent boom of autonomous driving nowadays has made object detection in traffic scenes a hot topic of research. Designed to classify and locate instances in the image, this is a basic but challenging task in the computer vision field. With its powerful feature extraction abilities, which are vital for object detection, deep learning has expanded its application areas to this field during the past several years and thus achieved breakthroughs. However, even with such powerful approaches, traffic scenarios have their own specific challenges, such as real-time detection, changeable weather, and complex lighting conditions. This survey is dedicated to summarizing research and papers on applying deep learning to the transportation environment in recent years. More than 100 research papers are covered, and different aspects such as key generic object detection frameworks, categorized object detection applications in traffic scenario, evaluation metrics, and classified datasets are included. Some open research fields are also provided. We believe that it is the first survey focusing on deep learning-based object detection in traffic scenario.
- Published
- 2021