# Deep Learning for Logo Detection: A Survey

arXiv: https://arxiv.org/abs/2210.04399 · HTML: https://mlsanity.com/paper/2210.04399/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 33 of its 122 references that are in the map.
- First posted on arXiv 10 Oct 2022.

## Builds on (most cited first, 30 of 33)

- [Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks](https://mlsanity.com/paper/1506.01497/) (2015)
- [End-to-End Object Detection with Transformers](https://mlsanity.com/paper/2005.12872/) (2020)
- [Feature Pyramid Networks for Object Detection](https://mlsanity.com/paper/1612.03144/) (2016)
- [You Only Look Once: Unified, Real-Time Object Detection](https://mlsanity.com/paper/1506.02640/) (2015)
- [SSD: Single Shot MultiBox Detector](https://mlsanity.com/paper/1512.02325/) (2015)
- [Fast R-CNN](https://mlsanity.com/paper/1504.08083/) (2015)
- [YOLOv3: An Incremental Improvement](https://mlsanity.com/paper/1804.02767/) (2018)
- [Rich feature hierarchies for accurate object detection and semantic segmentation](https://mlsanity.com/paper/1311.2524/) (2013)
- [YOLOv4: Optimal Speed and Accuracy of Object Detection](https://mlsanity.com/paper/2004.10934/) (2020)
- [YOLO9000: Better, Faster, Stronger](https://mlsanity.com/paper/1612.08242/) (2016)
- [YOLOX: Exceeding YOLO Series in 2021](https://mlsanity.com/paper/2107.08430/) (2021)
- [YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors](https://mlsanity.com/paper/2207.02696/) (2022)
- [YOLOv6: A Single-Stage Object Detection Framework for Industrial Applications](https://mlsanity.com/paper/2209.02976/) (2022)
- [You Only Look One-level Feature](https://mlsanity.com/paper/2103.09460/) (2021)
- [You Only Learn One Representation: Unified Network for Multiple Tasks](https://mlsanity.com/paper/2105.04206/) (2021)
- [LogoDet-3K: A Large-Scale Image Dataset for Logo Detection](https://mlsanity.com/paper/2008.05359/) (2020)
- [Logo-2K+: A Large-Scale Logo Dataset for Scalable Logo Classification](https://mlsanity.com/paper/1911.07924/) (2019)
- [Deep Learning Logo Detection with Data Expansion by Synthesising Context](https://mlsanity.com/paper/1612.09322/) (2016)
- [Open Set Logo Detection and Retrieval](https://mlsanity.com/paper/1710.10891/) (2017)
- [Open Logo Detection Challenge](https://mlsanity.com/paper/1807.01964/) (2018)
- [LOGO-Net: Large-scale Deep Logo Detection and Brand Recognition with Deep Region-based Convolutional Networks](https://mlsanity.com/paper/1511.02462/) (2015)
- [AttentionMask: Attentive, Efficient Object Proposal Generation Focusing on Small Objects](https://mlsanity.com/paper/1811.08728/) (2018)
- [FoodLogoDet-1500: A Dataset for Large-Scale Food Logo Detection via Multi-Scale Feature Decoupling Network](https://mlsanity.com/paper/2108.04644/) (2021)
- [DeepLogo: Hitting Logo Recognition with the Deep Neural Network Hammer](https://mlsanity.com/paper/1510.02131/) (2015)
- [Deep Learning for Logo Recognition](https://mlsanity.com/paper/1701.02620/) (2017)
- [Scalable Logo Recognition using Proxies](https://mlsanity.com/paper/1811.08009/) (2018)
- [The Open Brands Dataset: Unified brand detection and recognition at scale](https://mlsanity.com/paper/2012.07350/) (2020)
- [Improving Small Object Proposals for Company Logo Detection](https://mlsanity.com/paper/1704.08881/) (2017)
- [Automatic Graphic Logo Detection via Fast Region-based Convolutional Networks](https://mlsanity.com/paper/1604.06083/) (2016)
- [An Effective and Robust Detector for Logo Detection](https://mlsanity.com/paper/2108.00422/) (2021)

## Built on by (most cited first, 8 of 8)

- [Brand Visibility in Packaging: A Deep Learning Approach for Logo Detection, Saliency-Map Prediction, and Logo Placement Analysis](https://mlsanity.com/paper/2403.02336/) (2024)
- [RL-LOGO: Deep Reinforcement Learning Localization for Logo Recognition](https://mlsanity.com/paper/2312.16792/) (2023)
- [FashionLOGO: Prompting Multimodal Large Language Models for Fashion Logo Embeddings](https://mlsanity.com/paper/2308.09012/) (2023)
- [A Cross-direction Task Decoupling Network for Small Logo Detection](https://mlsanity.com/paper/2305.02503/) (2023)
- [An Augmentation-based Model Re-adaptation Framework for Robust Image Segmentation](https://mlsanity.com/paper/2409.09530/) (2024)
- [ExposureEngine: Oriented Logo Detection and Sponsor Visibility Analytics in Sports Broadcasts](https://mlsanity.com/paper/2510.04739/) (2025)
- [WildSVG: Towards Reliable SVG Generation Under Real-Word Conditions](https://mlsanity.com/paper/2602.21416/) (2026)
- [LU-500: A Logo Benchmark for Concept Unlearning](https://mlsanity.com/paper/2607.24101/) (2026)

## Abstract (arXiv)

When logos are increasingly created, logo detection has gradually become a research hotspot across many domains and tasks. Recent advances in this area are dominated by deep learning-based solutions, where many datasets, learning strategies, network architectures, etc. have been employed. This paper reviews the advance in applying deep learning techniques to logo detection. Firstly, we discuss a comprehensive account of public datasets designed to facilitate performance evaluation of logo detection algorithms, which tend to be more diverse, more challenging, and more reflective of real life. Next, we perform an in-depth analysis of the existing logo detection strategies and the strengths and weaknesses of each learning strategy. Subsequently, we summarize the applications of logo detection in various fields, from intelligent transportation and brand monitoring to copyright and trademark compliance. Finally, we analyze the potential challenges and present the future directions for the development of logo detection to complete this survey.