FoodLogoDet-1500: A Dataset for Large-Scale Food Logo Detection via Multi-Scale Feature Decoupling Network

食品Logo检测作为Logo检测的一项重要任务，可应用于健康饮食推荐、食品商标侵权纠纷、食品广告投放、超市自助结账系统等方面。然而，目前还没有用于食品Logo检测的数据集，为此本文提出了一个全新的大规模公共可用的食品Logo数据集，其中包括1500个类别，大约10万张图片和大约15万个手工标注的食品Logo。并进一步提出了一种多尺度特征解耦的食品Logo检测网络，将网络解耦成分类和回归两个分支并且在分类分支中使用特征偏移模块，可以有效地获得最具有代表性的分类特征，并引入平衡的特征金字塔，更加关注多尺度特征的全局信息，提高检测性能。我们在提出的数据集和另外两个公共Logo数据集上进行了大量实验，证明了该方法的有效性。数据集和代码下载地址：https://github.com/hq03/FoodLogoDet-1500-Dataset

Abstract

Food logo detection plays an important role in the multimedia for its wide real-world applications, such as food recommendation of the self-service shop and infringement detection on e-commerce platforms. A large-scale food logo dataset is urgently needed for developing advanced food logo detection algorithms. However, there are no available food logo datasets with food brand information. To support efforts towards food logo detection, we introduce the dataset FoodLogoDet-1500, a newlarge-scale publicly available food logo dataset, which has 1,500 categories, about 100,000 images and about 150,000 manually annotated food logo objects. We describe the collection and annotation process of FoodLogoDet-1500, analyze its scale and diversity, and compare it with other logo datasets. To the best of our knowledge, FoodLogoDet-1500 is the first largest publicly available high-quality dataset for food logo detection. The challenge of food logo detection lies in the large-scale categories and similarities between food logo categories. For that, we propose a novel food logo detection method Multi-scale Feature Decoupling Network (MFDNet), which decouples classification and regression into two branches and focuses on the classification branch to solve the problem of distinguishing multiple food logo categories. Specifically, we introduce the feature offset module, which utilizes the deformation-learning for optimal classification offset and can effectively obtain the most representative features of classification in detection. In addition, we adopt a balanced feature pyramid in MFDNet, which pays attention to global information, balances the multi-scale feature maps, and enhances feature extraction capability. Comprehensive experiments on FoodLogoDet-1500 and other two popular benchmark logo datasets demonstrate the effectiveness of the proposed method. The code and FoodLogoDet-1500 can be found at https://github.com/hq03/FoodLogoDet-1500-Dataset.

• Qiang Hou, Weiqing Min, Jing Wang, Sujuan Hou, Yuanjie Zheng, and Shuqiang Jiang. “FoodLogoDet-1500: A Dataset for Large-Scale Food Logo Detection via Multi-Scale Feature Decoupling Network”, 29th ACM International Conference on Multimedia (ACM Multimedia 2021), Chengdu, China, October 20-24, 2021.