Edge AI–Powered Object Recognition System for Smart Conveyor Sorting

A Robust Edge AI Pipeline

As a specialized machine learning development company, Softeq engineered a smart sorting conveyor system that merges computer vision with ruggedized industrial control logic.

The hardware architecture relies on the professional-grade reliability of the Arduino Opta, an industrial IoT PLC, paired with the Arduino UNO Q for dedicated vision processing. 

Edge AI & Computer Vision (with Edge Impulse)

Softeq managed the end-to-end machine learning pipeline using Edge Impulse. Our role as a computer vision development company involved:

• Dataset Engineering: Curating and labeling object images for industrial accuracy.
• Model Optimization: Tuning the machine vision model to run natively on the Arduino UNO Q.
• On-Device Deployment: Compiling an optimized Edge Impulse package that eliminates the need for external cloud processing, ensuring data remains secure and local.

The trained model was deployed to the Arduino UNO Q, enabling real-time, on-device object recognition without cloud dependency.

AI-Driven Sorting Logic

We established a seamless communication bridge between the AI "brain" and the mechanical "muscles." When an object is detected:

1. The Arduino UNO Q classifies the item via the Edge AI model.
2. A signal is instantly sent to the Arduino Opta.
3. The Opta executes the physical command: forwarding valid items or reversing the motor to reject anomalies.

AI Safety Layer (Manual Mode Override)

A standout feature of this industrial IoT solution is the intelligent safety sentry. Even when the system is switched to manual mode via a BLE controller, the Edge AI model remains active in the background. If a human operator inadvertently attempts to route an "invalid" object to the wrong destination, the AI triggers a manual override. This safety layer acts as a digital fail-safe, ensuring that human error cannot compromise the integrity of the sorting process.

Scalability with Edge AI

The solution is designed to be flexible and easy to adapt.

Using Edge Impulse, the model can be retrained with new data and redeployed to the same hardware. This allows the system to support new object types and use cases without any changes to the physical setup.

The Result: A Future-Proof Industrial PoC

Softeq successfully delivered a fully functional Proof of Concept (PoC) that proved the viability of Edge AI in high-stakes manufacturing. By combining the rock-solid hardware of Arduino Pro with sophisticated ML models, the project achieved:

• Validated Reliability:

  The system demonstrated that real-time machine learning development can be successfully applied to embedded hardware without performance degradation.

• Reduced Human Error:

  The AI Safety Layer provided a unique value proposition, ensuring consistent sorting quality regardless of operator experience.

• Infinite Scalability:

  Because the system uses Edge Impulse, the client can retrain the model for new product lines and redeploy it to the existing hardware without expensive physical retooling.