# DeepLogo: Hitting Logo Recognition with the Deep Neural Network Hammer

arXiv: https://arxiv.org/abs/1510.02131 · HTML: https://mlsanity.com/paper/1510.02131/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Oct 2015.

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

- [FoodLogoDet-1500: A Dataset for Large-Scale Food Logo Detection via Multi-Scale Feature Decoupling Network](https://mlsanity.com/paper/2108.04644/) (2021)
- [Deep Learning for Logo Detection: A Survey](https://mlsanity.com/paper/2210.04399/) (2022)
- [Multi-Label Logo Recognition and Retrieval based on Weighted Fusion of Neural Features](https://mlsanity.com/paper/2205.05419/) (2022)
- [Discriminative Semantic Feature Pyramid Network with Guided Anchoring for Logo Detection](https://mlsanity.com/paper/2108.13775/) (2021)
- [FashionLOGO: Prompting Multimodal Large Language Models for Fashion Logo Embeddings](https://mlsanity.com/paper/2308.09012/) (2023)
- [Classification of Beer Bottles using Object Detection and Transfer Learning](https://mlsanity.com/paper/2201.03791/) (2022)
- [Contrastive Multi-View Textual-Visual Encoding: Towards One Hundred Thousand-Scale One-Shot Logo Identification](https://mlsanity.com/paper/2211.12926/) (2022)

## Abstract (arXiv)

Recently, there has been a flurry of industrial activity around logo recognition, such as Ditto's service for marketers to track their brands in user-generated images, and LogoGrab's mobile app platform for logo recognition. However, relatively little academic or open-source logo recognition progress has been made in the last four years. Meanwhile, deep convolutional neural networks (DCNNs) have revolutionized a broad range of object recognition applications. In this work, we apply DCNNs to logo recognition. We propose several DCNN architectures, with which we surpass published state-of-art accuracy on a popular logo recognition dataset.