A survey of image object detection algorithm based on deep learning - Tingting ZHANG; Jianwu ZHANG; Chunsheng GUO; Huahua CHEN; Di ZHOU; Yansong WANG; Aihua XU (Dianxin kexue)
Image object detection is to find out the objects of interest in the image and determine their classifications and locations.It is a research hotspot in the field of computer vision.In recent years,due to the significant improvement in the accuracy of image classification with deep learning,image object detection models based on deep learning have gradually became mainstream.Firstly,the convolutional neural networks commonly used in image object detection were briefly introduced.Then,the existing classical image object detection models were reviewed from the perspective of candidate regions,regression and anchor-free methods.Finally,according to the detection results on the public dataset,the advantages and disadvantages of the models were analyzed,the problems in the image object detection research were summarized and the future development was forecasted.