Deep Learning for Generic Object Detection: A Survey

TL;DR

This survey comprehensively reviews recent advances in deep learning-based generic object detection, covering frameworks, feature representation, proposal generation, context modeling, training, and evaluation, highlighting future research directions.

Contribution

It provides an extensive overview of over 300 research contributions in deep learning for object detection, summarizing key methods and identifying future challenges.

Findings

Deep learning has significantly advanced object detection performance.

Various detection frameworks and feature representations have been developed.

Future research directions include improved proposal methods and context modeling.

Abstract

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.