• Software for «smart» self-service checkout photo 3

Software for «smart» self-service checkout

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4RM has developed and tested software for self-service checkouts, based on artificial intelligence and computer vision. This solution allows to optimize the process of recognizing goods at the self-service checkout. Let's consider the principle of operation and advantages of the technology.

A trained neural network helps identify specific weight items, such as vegetables, fruit or candy, which may look similar. The neural network checks the camera image against a database of products and correctly identifies the item, preventing accidental or intentional user error.

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But let's not forget that behind the apparent ease of artificial intelligence is a serious labor of data-engineers. Initially, a video is created for each unique unit (SKU) of weight goods, which is further broken down into fragments with markup. After that, data sets (datasets) are formed, which are used to train the neural network. The size of a dataset for training 1 SKU is about 100 GB. The duration of training can take from several days to weeks, depending on the number of items. 

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It looks complicated, but once formed the base can be used on any self-service cash registers and retail facilities. Moreover, in the course of work there will be additional training, which will increase the accuracy of work and expand the product matrix.

The project on recognition of weight goods of different colors and different geometric shapes using YOLO neural network at self-service checkout has several unique features:

1. YOLO technology. The project uses YOLO, the latest version of the You Only Look Once algorithm, to instantly recognize objects of different colors and shapes. This allows the system to quickly and accurately identify a wide range of products.

2. Neural Network Training. The neural network is trained on a variety of data to recognize standard and exotic types of fruits and vegetables. Products with unusual shapes and shades are also recognized with high accuracy.

3. Improved accuracy. YOLO's deep learning and sophisticated algorithms can distinguish between similar products, reducing the likelihood of errors in costing. This improves the system's accuracy in real-world applications.

4. Efficiency and speed. The system processes a large amount of information in a short time, speeding up the payment process at the checkout. This makes payment faster and more convenient for customers.

5. Scalability. The project is easily scalable and can be adapted for use in different retail outlets. From small stores to large supermarkets, the system is suitable for everyone.

6. Customer Satisfaction. Automating the product recognition process at the checkout reduces waiting time. This increases customer satisfaction and improves the shopping experience.

7. Competitive Advantages. Utilizing YOLO provides a number of competitive advantages including advanced detection capabilities and high processing speed. This ensures system accuracy and efficiency.

8. Strategic advantages. AI data analysis provides insights into consumer behavior and inventory management. This provides strategic advantages to retailers to improve operational efficiency.

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The 4RM team has tested the technology in practice and offers several models for its distribution for retailers. We offer both a hardware option, including Leda self-service checkout with pre-installed software, and a separate software purchase, on a one-time or subscription model. 4RM Systems' intelligent solution is compatible with any modern self-service cash register model. 

If you are interested in implementing cash registers with artificial intelligence or want to learn more about how they work, contact us by phone +375 44 558-44-44 or email info@4rm.org.

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