Overview
Advantech YOLO Vision Applications
About Advantech Container Catalog
Advantech Container Catalog is a comprehensive collection of ready-to-use, containerized software packages designed to accelerate the development and deployment of Edge AI applications. By offering pre-integrated solutions optimized for embedded hardware, it simplifies the challenges often faced with software and hardware compatibility, especially in GPU/NPU-accelerated environments.
Key benefits of the Container Catalog include:
| Feature / Benefit | Description |
|---|---|
| Accelerated Edge AI Development | Ready-to-use containerized solutions for fast prototyping and deployment |
| Hardware Compatibility Solved | Eliminates embedded hardware and AI software package incompatibility |
| GPU/NPU Access Ready | Supports passthrough for efficient hardware acceleration |
| Model Conversion & Optimization | Built-in AI model quantization and format conversion support |
| Optimized for CV & LLM Applications | Pre-optimized containers for computer vision and large language models |
| Open Ecosystem | 3rd-party developers can integrate new apps to expand the platform |
Container Overview
Advantech YOLO Vision Applications container provides a streamlined solution for running YOLOv8 computer vision applications on Advantech edge AI hardware. The toolkit automatically detects your device capabilities and sets up an optimized environment for computer vision tasks with full hardware acceleration support.
Designed specifically for Advantech edge AI devices accelerated by GPU such as EPC-R7300, and more, this toolkit enables rapid deployment of object detection, instance segmentation, and classification applications with minimal configuration required.
Container Demo
Object Detection
- Real-time object detection using YOLOv8
- Support for 80+ COCO dataset classes
- Configurable confidence thresholds and post-processing
Instance Segmentation
- Pixel-level object segmentation for precise boundary detection
- Multi-class segmentation capabilities
- Visualization tools for segmentation masks
Object Classification
- High-accuracy image classification
- Support for custom classification tasks
- Class confidence visualization
Key Features
Use Cases
This toolkit is ideal for:
| Feature | Description |
|---|---|
| Complete Docker Environment | Pre-configured container with all necessary hardware acceleration settings |
| Optimized Model Management | Tools for downloading and converting YOLOv8 models to accelerated formats |
| Hardware Acceleration Support | Full integration with NVIDIA CUDA, TensorRT, and GStreamer |
| X11 Display Support | Seamless visualization of model outputs directly from the container |
| Multiple Vision Applications | Ready-to-use apps for detection, segmentation, and classification |
Host Device Prerequisites
| Item | Specification |
|---|---|
| Compatible Hardware | Advantech GPU-accelerated devices - refer to Compatible hardware |
| Host OS | Ubuntu 20.04 |
| Required Software packages | *refer to below |
| Software Installation | Host Software Package Installation |
Container Environment Overview
Container Quick Start Guide
For Software Components on Container Image, container quick start, including docker-compose file, and more, please refer to Advantech EdgeSync Container Repository
Use Cases
This toolkit is ideal for:
| Application Area | Use Case Examples |
|---|---|
| Industrial Quality Inspection | Detect defects and inspect parts with instance segmentation |
| Smart Retail | Product recognition, customer behavior analysis |
| Smart Cities | Traffic monitoring, crowd analysis, object tracking |
| Security & Surveillance | Perimeter monitoring, intrusion detection |
| Agriculture | Crop monitoring, livestock tracking |
| Healthcare | Medical image analysis, equipment tracking |
| Robotics | Environmental perception, object manipulation guidance |