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Efficient and Consumer-Centered Item Detection and Classification with a Multicamera Network at High Ranges.

Nils Mandischer¹, Tobias Huhn¹, Mathias Hüsing¹

¹Machine Dynamics and Robotics (IGMR), Institute of Mechanism Theory, RWTH Aachen University, 52074 Aachen, Germany.

Sensors (Basel, Switzerland)

|July 24, 2021

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This study introduces a user-friendly method for robots to detect and classify items in industrial settings, achieving 84% accuracy. This enhances human-robot collaboration in the SHAREWORK project.

Area of Science:

Robotics and Automation
Computer Vision
Industrial Engineering

Background:

Human-robot collaboration is crucial for modern industrial environments.
Automated item detection and localization are key enablers for seamless human-robot interaction.
Existing methods often rely on complex deep learning, limiting user-friendliness and real-time application.

Purpose of the Study:

To develop and present a user-friendly pipeline for automated item detection and classification in industrial settings.
To support the EU project SHAREWORK by enabling robots to identify objects for collaborative tasks.
To achieve reliable and fast item recognition at significant ranges.

Main Methods:

A pipeline combining unsupervised segmentation and lenient machine learning for classification.

Keywords:

camera network classification computer vision machine learning segmentation unsupervised learning workspace cognition

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Utilizing established computer vision techniques adjusted for high-range (up to 8 meters) detection.

Full pipeline implementation including calibration, segmentation, and classification tailored for the industrial context.

Main Results:

The developed pipeline achieved a mean accuracy of 84% for item detection and classification.
The system operated at a processing speed of 0.85 Hz.
Validation was performed on a 40 sqm shop floor with up to nine different items and assemblies.

Conclusions:

The presented methodology offers a user-friendly and effective solution for item detection and classification in industrial human-robot collaboration.
The approach balances accuracy and speed, making it suitable for real-time applications.
This work contributes a validated pipeline to the SHAREWORK project, advancing collaborative robotics.