Deep Learning for Generic Object Detection: A Survey
It provides a comprehensive overview for researchers and practitioners in computer vision, but it is incremental as a survey rather than original research.
This survey paper compiles over 300 research contributions to review deep learning techniques for generic object detection in computer vision, summarizing frameworks, feature representation, and other aspects without presenting new experimental results.
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.