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

摘要：

目标检测是计算机视觉领域的重要研究任务，在机器人、自动驾驶、工业检测等方面应用广泛。在深度学习理论的基础上，系统性总结了目标检测算法的发展与研究现状，对两类算法的特点、优缺点和实时性进行对比。以交通场景中三类典型物体（非机动车、机动车和行人）为目标，从传统检测方法、目标检测算法、目标检测算法优化、三维目标检测、多模态目标检测和重识别六个方面分别论述和总结目标检测算法检测识别交通场景目标的研究现状与应用情况，重点介绍了各类方法的优势、局限性和适用场景。归纳了常用目标检测和交通场景数据集及评价标准，比较分析两类算法性能，展望目标检测算法在交通场景中应用研究的发展趋势，为智能交通、自动驾驶提供研究思路。

关键词: 目标检测, 深度学习, 交通场景, 计算机视觉, 自动驾驶