Object Detection Using Deep Learning Methods in Traffic Scenarios

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 break-throughs. 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.

Automated summary

The recent boom of autonomous driving has brought object detection in traffic scenes into focus, presenting challenges such as real-time detection, changeable weather, and complex lighting conditions. Deep learning has expanded into this field and achieved breakthroughs, covering key frameworks, categorized object detection applications, evaluation metrics, and datasets in more than 100 research papers. Open research fields also provide further avenues for exploration.