Šikić Franko, Kalafatić Zoran, Subašić Marko, Lončarić Sven
Image Processing Laboratory, Faculty of Electrical Engineering and Computing, University of Zagreb, 10000 Zagreb, Croatia.
Sensors (Basel). 2024 Jan 22;24(2):693. doi: 10.3390/s24020693.
The term out-of-stock (OOS) describes a problem that occurs when shoppers come to a store and the product they are seeking is not present on its designated shelf. Missing products generate huge sales losses and may lead to a declining reputation or the loss of loyal customers. In this paper, we propose a novel deep-learning (DL)-based OOS-detection method that utilizes a two-stage training process and a post-processing technique designed for the removal of inaccurate detections. To develop the method, we utilized an OOS detection dataset that contains a commonly used fully empty OOS class and a novel class that represents the frontal OOS. We present a new image augmentation procedure in which some existing OOS instances are enlarged by duplicating and mirroring themselves over nearby products. An object-detection model is first pre-trained using only augmented shelf images and, then, fine-tuned on the original data. During the inference, the detected OOS instances are post-processed based on their aspect ratio. In particular, the detected instances are discarded if their aspect ratio is higher than the maximum or lower than the minimum instance aspect ratio found in the dataset. The experimental results showed that the proposed method outperforms the existing DL-based OOS-detection methods and detects fully empty and frontal OOS instances with 86.3% and 83.7% of the average precision, respectively.
缺货（OOS）一词描述了一种情况，即购物者来到商店时，他们所寻找的产品不在指定货架上。商品缺货会造成巨大的销售损失，并可能导致声誉下降或失去忠实客户。在本文中，我们提出了一种基于深度学习（DL）的新型缺货检测方法，该方法采用两阶段训练过程和一种用于消除不准确检测结果的后处理技术。为了开发该方法，我们使用了一个缺货检测数据集，其中包含一个常用的完全空货架缺货类别和一个表示正面缺货的新类别。我们提出了一种新的图像增强程序，通过复制和镜像附近的产品来放大一些现有的缺货实例。首先仅使用增强后的货架图像对目标检测模型进行预训练，然后在原始数据上进行微调。在推理过程中，根据检测到的缺货实例的宽高比进行后处理。特别是，如果检测到的实例的宽高比高于数据集中发现的最大实例宽高比或低于最小实例宽高比，则将其丢弃。实验结果表明，所提出的方法优于现有的基于深度学习的缺货检测方法，分别以86.3%和83.7%的平均精度检测完全空货架和正面缺货实例。
