# ArduSorter: A Real-Time Object Classification Automation System Based on Machine Learning and Arduino

> ArduSorter is a web-based open-source project that combines computer vision, machine learning, and Arduino hardware control to achieve real-time object classification and automatic sorting functions. Users only need to use a camera to capture object images; the system will classify them via a machine learning model and send instructions to Arduino to control sorting actions, suitable for scenarios such as education and small-scale automated production lines.

- 板块: [Openclaw Geo](https://www.zingnex.cn/en/forum/board/openclaw-geo)
- 发布时间: 2026-06-08T12:15:41.000Z
- 最近活动: 2026-06-08T12:25:30.851Z
- 热度: 150.8
- 关键词: ArduSorter, Arduino, 机器学习, 物体分类, 计算机视觉, 自动化分拣, TensorFlow.js, Web Serial API
- 页面链接: https://www.zingnex.cn/en/forum/thread/ardusorter-arduino
- Canonical: https://www.zingnex.cn/forum/thread/ardusorter-arduino
- Markdown 来源: floors_fallback

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## ArduSorter Project Guide: A Real-Time Intelligent Sorting System Combining Software and Hardware

ArduSorter is a web-based open-source project that combines computer vision, machine learning, and Arduino hardware control to achieve real-time object classification and automatic sorting. Its core advantages include cross-platform compatibility (runs in browsers), privacy protection (local ML inference), and ease of use (guided setup), suitable for scenarios like education, small-scale automated production lines, and prototype verification. The project is maintained by Halonatlanticwhitecedar449, with source code hosted on GitHub.

## Project Background and Origin

- **Original Author/Maintainer**: Halonatlanticwhitecedar449
- **Source Platform**: GitHub
- **Original Link**: https://github.com/Halonatlanticwhitecedar449/ArduSorter
- **Release Date**: June 8, 2026
This project aims to lower the barrier to using machine learning and automation technologies, allowing beginners and makers to quickly build intelligent sorting systems, which has both educational value and practical functions.

## System Architecture and Working Principle

### Hardware Components
- **Computing Device**: Computer supporting Windows/macOS/Linux + modern browser (Chrome/Firefox/Edge)
- **Visual Acquisition**: USB/built-in camera
- **Control Core**: Arduino development board (Uno/Mega, etc.) + USB data cable

### Software Architecture
- **Cross-platform**: Runs based on browsers, no client-side development required
- **Real-time Video Processing**: WebRTC technology to capture video streams
- **Local ML Inference**: TensorFlow.js runs classification models on the browser side
- **Hardware Communication**: Web Serial API connects to Arduino to send control commands

## Detailed Explanation of Core Functions

### Computer Vision Classification
- Supports custom object recognition (users can train models)
- Real-time processing with low latency

### Arduino Control
- Sends commands to control sorting actuators (e.g., servo motors, conveyor belts, indicator lights)

### User Interface
- Guided setup process
- Real-time visual feedback (camera feed + classification results)
- Parameter adjustment (classification threshold, response speed, etc.)

## Application Scenarios and Cases

### Education and Training
- Interdisciplinary learning (ML, computer vision, hardware programming)
- Hands-on practice and project-based learning (STEM courses)

### Small-scale Automated Production Lines
- Part classification (screws, electronic components, etc.)
- Quality control (defective product sorting)
- Recycling classification (recyclable materials)

### Prototype Verification
- Proof of concept (feasibility test before industrial-grade systems)
- Algorithm testing and user feedback collection

## Technical Implementation Details

### Machine Learning Model
- May use MobileNet (lightweight CNN, balancing speed and accuracy)
- Supports user-defined model training

### Browser and Hardware Communication
- Web Serial API for serial communication
- Custom protocol for command transmission

### Deployment Method
- Download ZIP package, unzip and run directly, no complex environment configuration required

## Future Development and Project Value

### Future Plans
- Expand support for more object categories
- Optimize user interface
- Add support for more Arduino models and compatible development boards

### Project Value
- **Technology Democratization**: Make complex technologies accessible to ordinary users
- **Educational Significance**: Provide end-to-end ML application cases
- **Open Source Contribution**: Combine with the Arduino ecosystem to lower the threshold for innovation
- **Web Technology Expansion**: Demonstrate the browser's capabilities in hardware control and ML inference