随着深度学习在目标检测领域的大规模应用, 目标检测技术的精度和速度得到迅速提高, 已被广泛应用于行人检测、人脸检测、文字检测、交通标志及信号灯检测和遥感图像检测等领域. 本文在基于调研国内外相关文献的基础上对目标检测方法进行了综述. 首先介绍了目标检测领域的研究现状以及对目标检测算法进行检验的数据集和性能指标. 对两类不同架构的目标检测算法, 基于区域建议的双阶段目标检测算法和基于回归分析的单阶段目标检测算法的一些典型算法的流程架构、性能效果、优缺点进行了详细的阐述, 还补充了一些近几年来新出现的目标检测算法, 并列出了各种算法在主流数据集上的实验结果和优缺点对比. 最后对目标检测的一些常见应用场景进行说明, 并结合当前的研究热点分析了未来发展趋势.
With the large-scale application of deep learning in the field of object detection, the accuracy and speed of object detection technology have been rapidly improved, and it has been widely used in many fields, including pedestrian detection, face detection, text detection, traffic sign and signal light detection, and remote sensing image detection. This study reviews object detection technology based on the investigation of relevant domestic and foreign literature. First, the research status of object detection as well as the datasets and performance indicators for object detection algorithm tests are introduced. In this paper, two kinds of typical object detection algorithms with different architectures, namely two-stage object detection algorithms based on region proposals and one-stage object detection algorithms based on regression analysis, are described elaborately in their process architectures, performance effect, advantages, and disadvantages. In addition, some new object detection algorithms developed in recent years have been supplemented, and the experimental results and advantages and disadvantages of various algorithms on mainstream datasets are listed. Finally, some common application scenarios of object detection are specified, and future development trends are analyzed considering current research hotspots.