Supermarket Product Detection and Recognition: Utilizing Deep Learning with Rectified Imagery
In this paper, we try to supplement object detection models with traditional Hough transform (HT) and homogeneous estimation concepts.
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Key points
- Product Identification has sprung up to become one of the most challenging problems in the automation of the retail industry.
- Object identification models have emerged as a viable answer with their unprecedented identification and localization accuracy.
- We study the effect of rectified images using homography estimation and hough transform and their limitations on the problem of grocery identification.
- Extensive experiments on different object detection models suggest that image rectification of angled images improves the detection accuracy of grocery products in images.
Sources (1)
- [1]Supermarket Product Detection and Recognition: Utilizing Deep Learning with Rectified ImageryarXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 6, 10:44 AM
In this paper, we try to supplement object detection models with traditional Hough transform (HT) and homogeneous estimation concepts.
Product Identification has sprung up to become one of the most challenging problems in the automation of the retail industry.
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