LUO Hui-lan, CHEN Hong-kun
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.