Problem
Research questions and friction points this paper is trying to address.
Product Detection
Object Recognition
Image Rectification
Retail Automation
Densely Packed Images
Supermarket Product Detection and Recognition: Utilizing Deep Learning with Rectified Imagery
🤖 AI Summary
This study addresses the challenge that densely packed supermarket shelves induce highly variable viewing angles in product images, severely degrading the recognition accuracy of deep learning-based object detection models. To overcome this limitation, this work proposes a hybrid framework integrating classical geometric rectification with deep learning. Specifically, it employs Hough transform and homography estimation to perform perspective correction on tilted images, thereby enhancing downstream detection performance. Additionally, a multi-scenario dedicated dataset is constructed to support empirical validation. Experimental results demonstrate that the proposed rectification strategy significantly improves product detection accuracy. Furthermore, the study delineates the performance boundaries of this approach under extreme viewpoints and high-density occlusion scenarios, offering an effective new paradigm for retail vision tasks.
📝 Abstract
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.
Innovation
Methods, ideas, or system contributions that make the work stand out.
Image Rectification
Hough Transform
Homography Estimation
Object Detection
Supermarket Product Recognition
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