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6 September 2019 Product detection based on CNN and transfer learning
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With the development of artificial intelligence and the introduction of “new retail” concept, unmanned settlement has gradually become a research hotspot in academia and industry. As an important part of the retail, settlement is important for supermarket and user experience. In the traditional method, bar code based recognition requires a lot of manual assistance, and the salary cost is high; RFID also requires special equipment, and the hardware cost is high. At present, convolutional neural networks (CNNs) exhibit many advantages over traditional methods in various machine vision tasks such as image classification, object detection, instance segmentation, image generation, etc. Based on deep learning, this paper provides a novelty unmanned settlement solution that requires only a few cameras, which can achieve a new experience that is faster, more accurate and lower cost. A very high accuracy rate is achieved on our product dataset. The subsequent paper also demonstrate the effectiveness and the robustness of the algorithm under different conditions through a series of experiments.
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Xingsheng Zhu, Ming Liu, Yuejin Zhao, Liquan Dong, Mei Hui, and Lingqin Kong "Product detection based on CNN and transfer learning", Proc. SPIE 11137, Applications of Digital Image Processing XLII, 111371W (6 September 2019);

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