Supermarket product detection improves using image angle correction
Supermarket Product Detection and Recognition: Utilizing Deep Learning with Rectified Imagery
Computer Vision and Pattern RecognitionArtificial Intelligence
Summary
Identifying products on supermarket shelves is difficult because cameras capture them from different angles and shelves are packed tightly. The authors study how adjusting or "rectifying" these angled images helps improve the accuracy of automated product detection using deep learning. They combine traditional image techniques like the Hough transform with deep learning models and find that correcting the image angle can boost detection. However, the effectiveness depends on how tilted the image is and how many products are in the picture.
What this means in practice
- •For retail automation teams: Improve automated product recognition in supermarket shelf images by using image rectification to handle angled and crowded shelves.$Commercial implications: Enables more accurate inventory management systems that use corrected images, improving automation quality for retail software vendors.
- •For robotics developers: Enhance object detection in robotic vision systems navigating grocery stores with angled views and dense product arrangements.
Authors
Mayank Sah, Jimson Mathew
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.