Small and Dense Commodity Object Detection with Multi-Scale Receptive Field Attention

Abstract

Small and dense commodity object detection is highly valued to the applications in practical scenario. Unlike existing approaches mostly focus on detecting generic objects, this paper studies the problem of specific commodity detection, which is characterized by searching for small and dense instances with similar appearances. Since there is no available dataset or benchmark specialized for exploring this issue, we release a Small and Dense Object Dataset of Milk Tea (SDOD-MT) for promoting the research. Besides, our main solutions for mitigating the detection performance drop caused by the existence of small and dense objects can be concluded as two items. First, for the sake of highlighting the information of positive objects in the feature map, we propose a Multi-Scale Receptive Field (MSRF) attention to generate an attention map to weight the importance on each location of the image feature. Second, for eliminating the negative impact for detection performance brought by the issue of sample imbalance, we present a new loss function named ω-focal loss, which significantly improves the detection accuracy of the categories with few objects. Incorporating these two components into an end-to-end deep architecture, we propose a one-stage detecting framework, dubbed CommodityNet. Extensive experimental results on SDODMT demonstrate that the proposed approach achieves a superior performance on small dense object detection.