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

**Authors:** Shivang Agarwal, Jean Ogier Du Terrail, Frédéric Jurie  
**arXiv:** [1809.03193](https://arxiv.org/abs/1809.03193)  
**Date:** 2018-09-10  
**Summary page:** https://www.summarizepaper.com/en/paper/1809.03193v1/

## Key points

- Object detection has gained significant attention in the computer vision community over the past five years.
- Deep convolutional neural networks (DCNN) have played a crucial role in advancing object detection.
- The article titled "Recent Advances in Object Detection in the Age of Deep Convolutional Neural Networks" provides a comprehensive review of literature on object detection using deep CNNs.
- The article covers typical architectures used for object detection such as SSD, YOLO, and Faster-RCNN.
- It also explores challenges faced by the community and how object detection can be extended beyond its traditional scope.
- The survey examines public datasets commonly used for object detection research and discusses state-of-the-art algorithms associated with these datasets.
- This article serves as a valuable resource for researchers and practitioners working in the field of object detection with deep CNNs.

## Summary

Object detection, which involves detecting instances of objects in images, has gained significant attention in the computer vision community over the past five years. This interest can be attributed to both the importance and relevance of this task for various applications as well as the remarkable advancements made in this field since the introduction of deep convolutional neural networks (DCNN). In a recent article titled "Recent Advances in Object Detection in the Age of Deep Convolutional Neural Networks," authors Shivang Agarwal, Jean Ogier Du Terrail, and Frédéric Jurie provide a comprehensive review of literature on object detection using deep CNNs. The article offers an in-depth analysis of recent advances in this area. It covers not only typical architectures used for object detection such as SSD, YOLO, and Faster-RCNN but also delves into challenges currently faced by the community. Additionally, it explores how object detection can be extended beyond its traditional scope. Furthermore, this survey examines public datasets commonly used for object detection research and discusses state-of-the-art algorithms associated with these datasets. By providing a comprehensive overview of recent developments and challenges in object detection with deep CNNs, this article serves as a valuable resource for researchers and practitioners working in this field.