论文标题
开放品牌数据集：统一品牌检测和大规模认可
The Open Brands Dataset: Unified brand detection and recognition at scale
论文作者
论文摘要
由于全球电子商务平台的发展，知识产权保护（IPP）最近受到了越来越多的关注。品牌认可在IPP中起着重要作用。最新的品牌识别和检测研究基于探索新兴深度学习技术时不够全面的小型数据集。此外，评估现实和开放场景中品牌检测方法的真实性能是一项挑战。为了解决这些问题，我们首先定义了与通用对象检测相比，品牌检测和识别的特殊问题。其次，建立了一个名为“开放品牌”的新颖品牌基准。该数据集包含1,437,812张图像，这些图像具有品牌和50,000张图像，没有任何品牌。开放品牌的品牌的部分包含3,113,828个实例，其中包含3个维度：4种类型，559个品牌和1216个徽标。据我们所知，它是具有丰富注释的品牌检测和认可的最大数据集。我们提供有关数据集的深入综合统计数据，验证注释的质量，并研究许多现代模型的性能如何随越来越多的培训数据而发展。第三，我们设计了一个名为“品牌网”的网络来处理品牌识别。与现有检测方法相比，Brand Net获得了开放品牌的最先进地图。
Intellectual property protection(IPP) have received more and more attention recently due to the development of the global e-commerce platforms. brand recognition plays a significant role in IPP. Recent studies for brand recognition and detection are based on small-scale datasets that are not comprehensive enough when exploring emerging deep learning techniques. Moreover, it is challenging to evaluate the true performance of brand detection methods in realistic and open scenes. In order to tackle these problems, we first define the special issues of brand detection and recognition compared with generic object detection. Second, a novel brands benchmark called "Open Brands" is established. The dataset contains 1,437,812 images which have brands and 50,000 images without any brand. The part with brands in Open Brands contains 3,113,828 instances annotated in 3 dimensions: 4 types, 559 brands and 1216 logos. To the best of our knowledge, it is the largest dataset for brand detection and recognition with rich annotations. We provide in-depth comprehensive statistics about the dataset, validate the quality of the annotations and study how the performance of many modern models evolves with an increasing amount of training data. Third, we design a network called "Brand Net" to handle brand recognition. Brand Net gets state-of-art mAP on Open Brand compared with existing detection methods.
