关键词: 计算机视觉, 目标检测, 机器学习, 卷积神经网络

Abstract:

Object detection is the core of computer vision, which is widely used in image recognition, pedestrian detection, large-scale scene recognition and so on. The emergence of big data and the development of deep learning have injected new impetus into the research of object detection. Traditional target detection mainly uses the method based on manual features and machine learning, which is called feature-based. Present detection algorithm is mainly based on Convolutional Neural Network（CNN）. This paper, firstly analyzes the reason of the poor detection effect of feature-based method and puts forward the improved method. Then, the two-state method and one-state method derived from CNN network are described in detail, at the same time, the connection of each method and the improvement compared with the previous one will be clearly discussed. The principle and process of each network will be described in detail, then the analysis of shortcoming and the effects as well as improvements of the network are given. Finally, the detection effects of different methods on some data sets are included and the existing problems are pointed.

Key words: computer vision, object detection, machine learning, convolutional neural network