Goods shelf layout detection method and device and computer readable storage medium

Abstract

The invention provides a goods shelf layout detection method and device and a computer readable storage medium, and relates to the technical field of image processing, and the goods shelf layout detection method comprises the steps: obtaining a target image, collected by an image sensor, of a target goods shelf, wherein the target image comprises a depth image and an RGB image; performing shelf layering on the target shelf in the target image based on depth information in the depth image to obtain a shelf layering result; performing price tag detection on the target shelf based on the shelf layering result to obtain a price tag detection result; and determining the shelf layout of the target shelf based on the shelf layering result and the price tag detection result. The price tag position detection precision can be improved, the goods shelf layout detection does not need to use machine learning or deep learning to carry out goods identification, the calculation complexity is low, the goods shelf layout detection efficiency is improved, and real-time detection of the goods shelf layout can be realized.

Description

technical field [0001] The present invention relates to the technical field of image processing, in particular to a shelf layout detection method, device and computer-readable storage medium. Background technique [0002] With the development of the new retail model, retail has begun to focus on intelligent and digital operations, and traditional offline retailers lack process data for customers to select products. At present, by equipped with various sensors to independently and intelligently detect the interaction between customers and shelf products, it can help retail companies to operate finely, analyze consumer preferences, accurately locate products for sale, optimize category structure, increase sales, and gain shelf life. Layout is a necessary process to track, observe, record and analyze customers' shopping process. The existing shelf layout detection technology is usually based on the color front-view image acquired by the camera. By detecting straight lines or l...

AI Technical Summary

Problems solved by technology

The existing shelf layout detection technology is usually based on the color front-view image acquired by the camera. By detecting straight lines or line segments, combined with machine learning or deep learning to identify commodity categories, and realizing shelf commodity detection and positioning, it requires an expensive computing platform and a large number of SKU for training, poor real-time performance

Therefore, the existing shelf layout detection technology still has the problem of high computational complexity resulting in poor real-time detection

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