Real-Time Efficient Retail Object Recognition

With the development of machine learning technology, as a typical application of the new retail industry, unmanned retail store has developed rapidly. Using deep learning models for retail commodity recognition provides a new way to bring efficient checkout services. In this paper, we use YOLOv5 model for building an efficient retail object recognition system. First, in order to improve the computational efficience and lower the hardware cost, we propose to use an image quality detection module for pre-processing. Blurry images are discarded for improved recognition efficiency. Second, we adopt the convolutional neural network as the back-bone network for feature extraction and object recognition. Third, we implement a real-time recognition system in the lab environment for verification of our proposed system. Experiments on RPC dataset show that the proposed system can detect the location and category of retail goods in real-time. The model performance is promising for unmanned retail applications.