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English | 简体中文 | 启智OpenI | Gitee | Github
Currently in the process of actual operations of the retail industry, will produce a great human cost, such as guides, cleaning, settlement, and among them, especially need to spend a lot of labor cost and time cost in the identification of goods and settlement in the process of the price, and in the process, and so the customer need to wait in line. As a result, the retail industry has high labor costs and low work efficiency. Secondly, it also reduces the shopping experience of customers.
With the development of computer vision technology, as well as the unmanned and automated supermarket operation concept, the use of image recognition technology and target detection technology to achieve Automatic product identification and Automatic settlement demand, namely Automatic checkout system (ACO). The automatic checkout system based on computer vision can effectively reduce the operating cost of retail industry, improve the checkout efficiency of customers, so as to further enhance the user experience and happiness in the process of shopping.
ColugoMum——Intelligent Retail Rettlement PlatformCommitted to provide the largest offline retail experience store with retail settlement solution based on vision.
ColugoMum realize automatic settlement of goods purchased by users in the retail process. We take advantage of the PaddleClas team's open source PP-ShiTu technology, precise positioning of customers to buy goods, and intelligent, automatic price settlement. When customers place their chosen products in the designated area, ColugoMum can accurately locate and identify each product, and can return a complete shopping list and the actual total price of goods that customers should pay. When the system has a new product increase, the system only need to update the retrieval database, without retraining the model.
This project is a lightweight general PP-ShiTu image recognition system provides the solid ground application cases, the new one of the retail industry and retail visual intelligent solution provides a very good basis and train of thought, especially for solving many categories, small sample, high similarity, and frequently updated the special image recognition scene pain difficulties provides reference of demonstrations, Greatly reduce the retail industry in the actual operation of the huge human cost, improve the retail industry unmanned, automation, intelligent level.
ColugoMum Based on PaddleClas as the main feature development suite, leveraging its open source PP-ShiTu for core feature development. Through PaddleInference, it was deployed in Jetson Nano, and was packaged based on QPT to develop an industrial-level intelligent retail settlement platform in line with actual application requirements.
PP-ShiTu is a practical lightweight general image recognition system, which is mainly composed of three modules: subject detection, feature learning and vector retrieval. The system from the selection and adjustment of backbone networks, the choice of loss function, vector data, transform strategy, choice of regularization parameter, use the training model and quantitative model cutting eight aspects, use a variety of strategies, optimize the model of the various modules, finally got on the CPU is only 0.2 s to complete 10 w + library image recognition system.
The whole image recognition system is divided into three steps(See PP-ShiTu training module for details):
(1) The candidate regions of image objects are detected by a target detection model;
(2) Feature extraction for each candidate region;
(3) Feature matching with images in the retrieval database, and extraction of recognition results.
For the new unknown category, there is no need to retrain the model, but only need to add the image of the category in the retrieval database and rebuild the retrieval database to recognize the category.
【The first one】:Products-10K Large Scale Product Recognition Dataset
【The second one】:RP2K: A Large-Scale Retail Product Dataset for Fine-Grained Image Classification
ColugoMum based on the above two data sets and combined with the actual characteristics of the retail scene, adaptive processing is carried out.
东古酱油一品鲜
东古黄豆酱750G
东鹏特饮罐装
中华(硬)
中华(软)
乳酸菌600亿_2
乳酸菌600亿_3
乳酸菌600亿原味
乳酸菌600亿芒果
乳酸菌600亿芦荟
...
The processed dataset is now open source in AIStudio.
model | num epoch | batch size/gpu cards | learning rate | use cutout | use ssld | top1 recall | config |
---|---|---|---|---|---|---|---|
PP_LCNet_x2_5 | 400 | 256/4 | 0.01 | N | N | 98.189% | config |
PP_LCNet_x2_5 | 400 | 256/4 | 0.01 | Y | N | 98.21% | config |
PP_LCNet_x2_5 | 400 | 256/4 | 0.005 | N | N | 98.201% | config |
PP_LCNet_x2_5 | 400 | 256/4 | 0.005 | Y | N | 98.29% | config |
PP_LCNet_x2_5 | 400 | 256/4 | 0.001 | Y | N | 98.26% | config |
PP_LCNet_x2_5 | 400 | 64/4 | 0.005 | Y | Y | 98.30% | config |
PP_LCNet_x2_5 | 400 | 64/4 | 0.0025 | Y | Y | 98.37% | config |
PP_LCNet_x2_5 | 400 | 64/4 | 0.002 | N | Y | 98.38% | config |
PP_LCNet_x2_5 | 400 | 64/4 | 0.002 | Y | Y | 98.39% | config |
Attention:
ColugoMum has been connected toJetson Nano, Windows, linux system.
Windows
[ColugoMum provides a relatively simple demo version]
We use QPT for packaging.
Download the project code, enter the QPT_client folder, and Click the "启动程序.exe".
Linux
Download the project code, enter the client folder, and run the following code to run it :
python client.py
For details of the image recognition part deployment, you can see PP-ShiTu Development
Wechat applet
Open the wechat developer tool, import the AIContainer folder under the system folder and run it;
Main Interface
Client Side Interface
number | complete degree | priority | category | Functional description |
---|---|---|---|---|
1 | completed | ★★★★★ | Applets | |
2 | Doing | ★★★★★ | Applets | Initial function online |
3 | completed | ★★★★★ | Client Side | |
4 | planning | ★★★★ | Applets | Separation of functions for managers and customers |
5 | completed | ★★★★ | web | |
6 | planning | ★★★ | Applets | Realize the automatic entry of commodity name |
7 | planning | ★★ | APP | Enabling deployment on the IOS and Android |
Duty | Name |
---|---|
PM | X. Yan |
Algorithm | X. Yan |
Side of the front end | X. Yan |
Applets front end | C. Shen |
Back End | D. DU |
A cup of coffee will refresh your mind, and product updates will be faster and better!
We welcome you to contribute code or provide suggestions for "ColugoMum". Whether you have a bug, fix a bug, or add a new feature, feel free to submit Issue or Pull Requests.
@software{ColugoMum2021,
author = {Xin Yan, Chen Shen, and XuDong Du},
title = {{ColugoMum: Intelligent Retail Settlement Platform}},
howpublished = {\url{https://github.com/thomas-yanxin/Smart_container}},
year = {2021}
}
商超、无人零售自动化售卖解决方案,基于计算机视觉的自动结账系统能有效降低零售行业的运营成本,提高顾客结账效率,从而进一步提升用户在购物过程中的体验感与幸福感。
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