Recent Advances in Deep Learning for Object Detection

TL;DR

This paper provides a comprehensive survey of recent deep learning-based object detection methods, analyzing various frameworks, strategies, and benchmarks to guide future research in the field.

Contribution

It systematically reviews recent advances in deep learning for object detection, organizing existing work into detection components, learning strategies, and applications.

Findings

Analysis of detector architectures and their impact on performance

Discussion of learning strategies and data sampling techniques

Overview of benchmarks and future research directions

Abstract

Object detection is a fundamental visual recognition problem in computer vision and has been widely studied in the past decades. Visual object detection aims to find objects of certain target classes with precise localization in a given image and assign each object instance a corresponding class label. Due to the tremendous successes of deep learning based image classification, object detection techniques using deep learning have been actively studied in recent years. In this paper, we give a comprehensive survey of recent advances in visual object detection with deep learning. By reviewing a large body of recent related work in literature, we systematically analyze the existing object detection frameworks and organize the survey into three major parts: (i) detection components, (ii) learning strategies, and (iii) applications & benchmarks. In the survey, we cover a variety of factors affecting the detection performance in detail, such as detector architectures, feature learning, proposal generation, sampling strategies, etc. Finally, we discuss several future directions to facilitate and spur future research for visual object detection with deep learning. Keywords: Object Detection, Deep Learning, Deep Convolutional Neural Networks