Recent Advances in Object Detection in the Age of Deep Convolutional Neural Networks

TL;DR

This paper reviews recent progress in object detection using deep convolutional neural networks, highlighting architectures, challenges, datasets, and future directions in the field.

Contribution

It provides a comprehensive survey of recent deep CNN-based object detection methods, architectures, datasets, and challenges, offering an in-depth overview of the current state of the art.

Findings

Overview of key architectures like SSD, YOLO, Faster-RCNN

Discussion of current challenges in object detection

Summary of public datasets and algorithms

Abstract

Object detection-the computer vision task dealing with detecting instances of objects of a certain class (e.g., 'car', 'plane', etc.) in images-attracted a lot of attention from the community during the last 5 years. This strong interest can be explained not only by the importance this task has for many applications but also by the phenomenal advances in this area since the arrival of deep convolutional neural networks (DCNN). This article reviews the recent literature on object detection with deep CNN, in a comprehensive way, and provides an in-depth view of these recent advances. The survey covers not only the typical architectures (SSD, YOLO, Faster-RCNN) but also discusses the challenges currently met by the community and goes on to show how the problem of object detection can be extended. This survey also reviews the public datasets and associated state-of-the-art algorithms.