发表机构
Indian Institute of Technology, Patna（印度理工学院巴特那分校）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对超市货架密集图像因拍摄角度导致检测精度下降的问题，本文提出结合霍夫变换与单应性估计进行图像矫正，实验证明矫正能提升商品检测精度，但受角度和密度限制。
AI 中文摘要
商品识别已成为零售业自动化中最具挑战性的问题之一。随着工业5.0新标准的实施，自动化库存管理和目录创建任务变得至关重要。物体识别模型凭借其前所未有的识别和定位精度，已成为一种可行的解决方案。然而，超市货架紧密排列的设计导致了拍摄图像时角度变化的问题。角度变化且密集排列的图像（单张图像包含多个物体）对这些模型而言变得难以处理。在本文中，我们尝试将物体检测模型与传统的霍夫变换（HT）和单应性估计概念相结合。我们研究了使用单应性估计和霍夫变换进行图像矫正的效果及其在商品识别问题上的局限性。我们提出创建一个新数据集来测试此类矫正的效果，并对不同角度变化和每张图像物体密度的场景进行结果分析。对不同物体检测模型的大量实验表明，对倾斜图像进行图像矫正可提高图像中杂货产品的检测精度。结果还强调了矫正对图像拍摄角度和图像物体密度的局限性。
英文摘要
Product Identification has sprung up to become one of the most challenging problems in the automation of the retail industry. With the new industry 5.0 standards, automated inventory management, and catalog creation tasks are vitally important. Object identification models have emerged as a viable answer with their unprecedented identification and localization accuracy. However, the close-knit rack design of supermarkets generates the problem of angle variation in capturing images. The angle-variant densely packed images(a single image contains many objects) become overwhelming for these models alone. In this paper, we try to supplement object detection models with traditional Hough transform (HT) and homogeneous estimation concepts. We study the effect of rectified images using homography estimation and hough transform and their limitations on the problem of grocery identification. We make a case for creating a new dataset to test the effects of such rectification and produce analytical results on different scenarios of angle variation and object densities per image. Extensive experiments on different object detection models suggest that image rectification of angled images improves the detection accuracy of grocery products in images. The results also highlight the limitation of rectification on the angle of image capture and the object density of the image.
Comments10 Pages, 7 Figures, 5 Tables
