关键词: 目标检测, 深度学习, 交通场景, 计算机视觉, 自动驾驶

Abstract:

Object detection is an important research task in the field of computer vision. It is widely used in robotics, automatic vehicles, industrial detection and other fields. On the basis of deep learning theory, the development and research status of object detection algorithm are firstly systematically summarized and the characteristics, advantages, disadvantages and real-time performance of the two categories of algorithms are compared. Next to the three kinds of typical targets （non-motor vehicles, motor vehicles and pedestrians） as objects in the traffic scene, the research status and application of object detection algorithm for detecting and identifying objects are discussed and summarized respectively from six aspects in traffic scene：traditional detection method, object detection algorithm, object detection algorithm optimization, 3d object detection, multimodal object detection and re-identification. And the application of focus on the advantages, limitations and applicable scenario of various methods. Finally, the common object detection and traffic scene data sets and evaluation criteria are summarized, the performance of the two categories of algorithms is compared and analyzed, and the development trend of the application of object detection algorithm in traffic scenes is prospected, providing research ideas for intelligent traffic and automatic vehicles.

Key words: object detection, deep learning, traffic scene, computer vision, autonomous vehicles