Self-Supervised Fully Automatic Learning Machine for Intelligent Retail Container

Intelligent retail containers (IRCs) are a representative application of object detection in open-world scenarios. However, their wide application is limited because of labor-intensive data labeling and frequently updated retail goods categories. To address the two challenges, we propose a self-supervised fully automatic learning machine (SFLM), which consists of a self-paced active learning (SPAL) human-machine cooperation module and a clustering autolearning (CAL) module. Based on active learning and self-paced learning, we use the SPAL module to handle the labor-intensive data labeling task, which speeds up data labeling and reduces its cost. We further use the CAL module to overcome the challenge of frequently updated goods categories in intelligent retail. Considering the lack of large-scale datasets in real retail container scenarios, we propose a new challenging IRC dataset. This dataset consists of 35000 images, with 370000 bounding boxes and instance segmentation masks, covering 200 common categories of goods for different data acquisition settings. Extensive experimental results on the proposed dataset show that the proposed SFLM greatly reduced the labeling cost and time consumption and achieved superior performance in recognizing new categories. The dataset will be available at https://github.com/SunYM2020/IRC.