Abstract: Object detection is an important problem in computer vision, which has critical research value in the field of pedestrian tracking, license plate recognition and unmanned driving. In recent years, the accuracy of image classification is greatly improved with deep learning, thus the object detection methods based on deep learning have gradually become mainstream. The development and present situation of object detection methods are reviewed, and a prospect is made. Firstly, the development, improvement and deficiency of the traditional algorithms and depth learning-based algorithms are summarized, and then compared. Finally, the difficulties and challenges of the object detection method based on deep learning are discussed, and the possible development direction is prospected.

Key words: object detection, deep learning, computer vision, convolutional neural networks, object classification detection

摘要： 目标检测是计算机视觉中一个重要问题，在行人跟踪、车牌识别、无人驾驶等领域都具有重要的研究价值。近年来，随着深度学习对图像分类准确度的大幅度提高，基于深度学习的目标检测算法逐渐成为主流。梳理了目标检测算法的发展与现状，并作出展望：总结了传统算法与引入深度学习的目标检测算法的发展、改进与不足，并就此做出对比；最后讨论了基于深度学习的目标检测算法所存在的困难与挑战，并就可能的发展方向进行了展望。

关键词: 目标检测, 深度学习, 计算机视觉, 卷积神经网络, 目标分类检测