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

arXiv: https://arxiv.org/abs/1809.03193 · HTML: https://mlsanity.com/paper/1809.03193/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Sep 2018.

## Built on by (most cited first, 7 of 7)

- [Object Detection with Transformers: A Review](https://mlsanity.com/paper/2306.04670/) (2023)
- [YOLOv1 to YOLOv10: A comprehensive review of YOLO variants and their application in the agricultural domain](https://mlsanity.com/paper/2406.10139/) (2024)
- [Surround-View Cameras based Holistic Visual Perception for Automated Driving](https://mlsanity.com/paper/2206.05542/) (2022)
- [Few-shot 1/a Anomalies Feedback : Damage Vision Mining Opportunity and Embedding Feature Imbalance](https://mlsanity.com/paper/2307.12676/) (2023)
- [Meta-Learning for Color-to-Infrared Cross-Modal Style Transfer](https://mlsanity.com/paper/2212.12824/) (2022)
- [Generalized Mask-aware IoU for Anchor Assignment for Real-time Instance Segmentation](https://mlsanity.com/paper/2312.17031/) (2023)
- [UAV-Based Human Body Detector Selection and Fusion for Geolocated Saliency Map Generation](https://mlsanity.com/paper/2408.16501/) (2024)

## Abstract (arXiv)

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.