基于深度学习的通用目标检测研究综述

Object detection is one of the most fundamental and important tasks in the field of computer vision, which is the basis of high level vision tasks such as behavior recognition and human computer interaction. With the development of deep learning technology, the accuracy and efficiency of object detectors have been greatly improved. Compared with traditional object detection algorithms, deep learning utilizes powerful hierarchical feature extraction and learning capabilities to make breakthroughs in the performance of object detectors. Meanwhile, the large scale datasets and the tremendous improvement in computing power have also contributed to the vigorous development in this field. In this paper, the existing research of object detectors based on deep learning are reviewed in detail. First, we review the traditional object detection algorithms and its problems. Then, object detectors based on deep learning are introduced, and the region based and single stage benchmark detectors are summarized. After that, the current mainstream object detectors are concluded from eight perspectives of feature maps, context information, bounding box optimization, regional proposal, category imbalance processing, training strategy, weakly supervised learning and unsupervised learning. Finally, the problems to be solved in the object detectors are proposed and future research directions are prospected.