Abstract
Out-of-stock (OOS) problem is a significant reason of the decline of goods sales in offline supermarkets since the frequent lack of goods on the shelves can reduce the enthusiasm of shoppers. For this purpose, it is necessary to effectively detect the OOS situation, which can ensure that the products are replenished in time. In this paper, an out-of-stock detection method based on deep learning is proposed. We first introduce the Faster R-CNN algorithm to obtain location information. Then the Faster R-CNN algorithm is followed by three out-of-stock detection methods: canny operator, gray level co-occurrence matrix, and color features for out-of-stock detection. The experimental results show that the method based on canny operator performs better, which can achieve a recall rate of 83.9%, a precision rate of 91.7% and a F1-Measure of 87.6% in real scene dataset.
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Acknowledgement
This work was supported by the grants of the National Science Foundation of China (Grant Nos. 61472467 and 61672011) and the National Key R&D Program of China (2017YFC1311003).
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Chen, J., Wang, SL., Lin, HL. (2019). Out-of-Stock Detection Based on Deep Learning. In: Huang, DS., Bevilacqua, V., Premaratne, P. (eds) Intelligent Computing Theories and Application. ICIC 2019. Lecture Notes in Computer Science(), vol 11643. Springer, Cham. https://doi.org/10.1007/978-3-030-26763-6_22
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DOI: https://doi.org/10.1007/978-3-030-26763-6_22
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