Object detection is a hot topic in the field of computer vision

and has been widely used in robot navigation

intelligent video surveillance

aerospace

and other fields. The research background

significance and challenges of object detection were introduced. Then the object detection algorithms based on deep learning were reviewed according to two categories: candidate region-based and regression-based. For the candidate region-based algorithms

we first introduced the R-CNN (Region with Convolutional Neural Network) based series of algorithms

and then the R-CNN based methods were overviewed from four dimensions: the research of feature extraction networks

the region of interesting pooling researches

improved works based on region proposal networks

and some improved approaches of non maximum suppression algorithms. Next

the regression-based algorithms were surveyed in terms of YOLO (You Only Look Once) series and SSD (Single Shot multibox Detector) series. Finally

according to the current trend of object detection algorithms that are developing more efficient and reasonable detection frameworks

the future research focuses of unsupervised and unknown category object detection directions were prospected.