Take Goods from Shelves: A dataset for class-incremental object detection

Abstract

Object detection for automatic visual checkout in self-service vending machines is attracting significant attention in the retail industry. However, several critical challenges have not received enough attention. First, large-scale, high-quality retail image datasets are urgently demanded to train and evaluate the detection models. Second, the trained models should be able to cope with the frequently added new goods at low cost, while most cutting-edge models cannot. In this paper, we propose a new hierarchical large-scale object detection dataset, called Take Goods from Shelves (TGFS), containing 38K images of 24 fine-grained and 3 coarse classes. A preliminary method for solving the goods-adding problem, called Faster R-CNN Class-incremental Object Detector (FCIOD), is also described and evaluated. In addition, several popular methods are benchmarked on the TGFS dataset.