基于深度卷积网络的目标检测综述

Deep convolutional network is prevalent in object detection task. Region-based convolutional neural network(RCNN) bridges the gap between the classification of deep convolutional network and the object detection task well. Then the whole object detection process is aggregated into a unified deep framework by Faster-RCNN. You only look once(YOLO) and single shot multibox detector(SSD) effectively improve the efficiency of object detection. Different deep object detection frameworks are comprehensively analyzed and divided into two categories: the proposal based framework and the regression based framework. The proposal based framework is utilized to generate thousands of candidate proposals and then classification and bounding box regression are conducted on these proposals. The regression based framework outputs the bounding box position through some special iterations directly. Furthermore, the advantage for different kinds of frameworks is demonstrated through adequate experiments on the mainstream database like PASCAL_VOC and COCO. Finally, the development direction of object detection is discussed.