Overview
The Automated Self-Checkout Reference Implementation provides critical components required to build and deploy a self-checkout use case using Intel® hardware, software, and other open source software. This reference implementation provides a pre-configured automated self-checkout pipeline optimized for Intel® hardware.
Requirements
To build the Intel® Automated Self-Checkout Reference Implementation, you need:
- Ubuntu* LTS Boot Device
- Docker*
- Git*
To know about the supported platforms, see the list of platforms.
Learning Objectives
Using this reference implementation, you can:
- Identify optimized middleware and frameworks relevant for checkout use cases from Intel.
- Use the core checkout services to build the automated self-checkout reference modules.
- Identify the required hardware for the intended workload.
Features and Benefits
With this reference implementation, the self-checkout stations can:
- Recognize the non-barcoded items faster.
- Recognize the product SKU and items placed in transparent bags.
- Reduce the steps involved in identifying products when there is no exact match (top five choices)
How it Works
The video stream is cropped and resized to enable the inference engine to run the associated models. The object detection and product classification features identify the SKUs during checkout. The bar code detection, text detection, and recognition features further verify and increase the accuracy of the detected SKUs. The inference details are then aggregated and pushed to the enterprise service bus or MQTT to process the combined results further.
Resources
The reference implementation includes:
- Source code
- Microservices
- Benchmark scripts
- Pre-trained models
- Documentation
- Learning videos (will be available in the forthcoming releases)
- Hardware recommendation
- Tools and libraries
- Operating system support
- Support for Intel architecture-based platforms
Performance Results
Find the latest performance results by choice of Intel® processors for the vision-enabled workloads.
Report Issue or Submit Feedback
You can open an issue on GitHub to report a problem related to the reference implementation or give feedback.