Object detection, one of the most fundamental and challenging problems in computer vision, seeks to locate object instances from a large number of predefined categories in natural images. Deep learning techniques have emerged as a powerful strategy for learning feature representations directly from data and have led to remarkable breakthroughs in the field of generic object detection. Given this period of rapid evolution, the goal of this paper is to provide a comprehensive survey of the recent achievements in this field brought about by deep learning techniques. More than 300 research contributions are included in this survey, covering many aspects of generic object detection: detection frameworks, object feature representation, object proposal generation, context modeling, training strategies, and evaluation metrics. We finish the survey by identifying promising directions for future research.
@article{arxiv.1809.02165,
title = {Deep Learning for Generic Object Detection: A Survey},
author = {Li Liu and Wanli Ouyang and Xiaogang Wang and Paul Fieguth and Jie Chen and Xinwang Liu and Matti Pietikäinen},
journal= {arXiv preprint arXiv:1809.02165},
year = {2019}
}