To make full use of computer vision technology in stores, it is required to consider the actual needs that fit the characteristics of the retail scene. Pursuing this goal, we introduce the United Retail Datasets (Unitail), a large-scale benchmark of basic visual tasks on products that challenges algorithms for detecting, reading, and matching. With 1.8M quadrilateral-shaped instances annotated, the Unitail offers a detection dataset to align product appearance better. Furthermore, it provides a gallery-style OCR dataset containing 1454 product categories, 30k text regions, and 21k transcriptions to enable robust reading on products and motivate enhanced product matching. Besides benchmarking the datasets using various state-of-the-arts, we customize a new detector for product detection and provide a simple OCR-based matching solution that verifies its effectiveness.
翻译:为了在商店中充分利用计算机视觉技术,需要考虑符合零售场景特点的实际需求。为了实现这一目标,我们引入了联合零售数据集(United Retail Datats),这是对检测、阅读和匹配的算法构成挑战的产品的基本视觉任务的一个大规模基准。有了1.8M四边形实例附加说明,Unitail提供了检测数据集,以更好地调整产品外观。此外,它提供了一张画廊式的OCR数据集,包含1454类产品、30k文本区域以及21k个抄录,以便能够对产品进行稳健的阅读和激励强化的产品匹配。除了利用各种最新工艺对数据集进行基准评估外,我们还定制了产品检测的新探测器,并提供简单的OCR匹配解决方案,以验证其有效性。