Integration of Deep Learning Vision Systems in Collaborative Robotics for Real-Time Applications

Collaborative robotics and computer vision systems are increasingly important in automating complex industrial tasks with greater safety and productivity. This work presents an integrated vision system powered by a trained neural network and coupled with a collaborative robot for real-time sorting and quality inspection in a food product conveyor process. Multiple object detection models were trained on custom datasets using advanced augmentation techniques to optimize performance. The proposed system achieved a detection and classification accuracy of 98%, successfully processing more than 600 items with high efficiency and low computational cost. Unlike conventional solutions that rely on ROS (Robot Operating System), this implementation used a Windows-based Python framework for greater accessibility and industrial compatibility. The results demonstrated the reliability and industrial applicability of the solution, offering a scalable and accurate methodology that can be adapted to various industrial applications.

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Publication Details

Journal
Applied Sciences
Published
2025-01-27
DOI
https://doi.org/10.3390/app15031336
Citations
10
Primary Topic
Robotics and Automated Systems
Type
article
Field-Weighted Citation Impact
12.15
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Integration of Deep Learning Vision Systems in Collaborative Robotics for Real-Time Applications

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10 citations
Applied Sciences
Robotics and Automated Systems
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Integration of Deep Learning Vision Systems in Collaborative Robotics for Real-Time Applications

Laurențiu Adrian Cartal, Tudor Cătălin Apostolescu, José Machado, António M. Lopes, Adriano A. Santos, Florentina Badea, Filipe Pereira, António Ferreira da Silva, António Ramos Silva, Nuno Terras
article en
10 citations

Abstract

Collaborative robotics and computer vision systems are increasingly important in automating complex industrial tasks with greater safety and productivity. This work presents an integrated vision system powered by a trained neural network and coupled with a collaborative robot for real-time sorting and quality inspection in a food product conveyor process. Multiple object detection models were trained on custom datasets using advanced augmentation techniques to optimize performance. The proposed system achieved a detection and classification accuracy of 98%, successfully processing more than 600 items with high efficiency and low computational cost. Unlike conventional solutions that rely on ROS (Robot Operating System), this implementation used a Windows-based Python framework for greater accessibility and industrial compatibility. The results demonstrated the reliability and industrial applicability of the solution, offering a scalable and accurate methodology that can be adapted to various industrial applications.

Applied SciencesVol. 15(3)
Titu Maiorescu University (RO), Technical University of Cluj-Napoca (RO), Universidade do Porto (PT), Institute of Mechanical Engineering and Industrial Mangement (PT), National Institute of Research and Development for Electrochemistry and Condensed (RO), Universitatea Națională de Știință și Tehnologie Politehnica București (RO), Polytechnic Institute of Porto (PT), University of Minho (PT)
Openalex Percentile: Top 1%
Robotics and Automated Systems
12.15
DOIOpen Access PDFOpenAlex
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