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

• Type: Article in International Scientific Journal
• Date: 2025-01-27

Authors

Filipe Pereira (Author) FEUP

Journal

Title: Applied Sciences

Vol. 15

Final page: 1336

Publisher: MDPI

Indexing

ISI Web of Knowledge - 1 Citation

Scopus - 1 Citation

Other information

Authenticus ID: P-018-1JY

DOI: 10.3390/app15031336

Abstract (EN): 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.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 39