PreprintArticleVersion 1Preserved in Portico This version is not peer-reviewed
Developing a YOLOv3-Based Neural Network to Enable Digital Cameras to Detect Features on the Reflective Surface of Objects in Robotic Applications: A Low-Computational Approach with a New Public Dataset
Version 1
: Received: 23 February 2024 / Approved: 4 March 2024 / Online: 5 March 2024 (13:21:58 CET)
How to cite:
Bajrami, A.; Palpaceli, M.C. Developing a YOLOv3-Based Neural Network to Enable Digital Cameras to Detect Features on the Reflective Surface of Objects in Robotic Applications: A Low-Computational Approach with a New Public Dataset. Preprints2024, 2024030221. https://doi.org/10.20944/preprints202403.0221.v1
Bajrami, A.; Palpaceli, M.C. Developing a YOLOv3-Based Neural Network to Enable Digital Cameras to Detect Features on the Reflective Surface of Objects in Robotic Applications: A Low-Computational Approach with a New Public Dataset. Preprints 2024, 2024030221. https://doi.org/10.20944/preprints202403.0221.v1
Bajrami, A.; Palpaceli, M.C. Developing a YOLOv3-Based Neural Network to Enable Digital Cameras to Detect Features on the Reflective Surface of Objects in Robotic Applications: A Low-Computational Approach with a New Public Dataset. Preprints2024, 2024030221. https://doi.org/10.20944/preprints202403.0221.v1
APA Style
Bajrami, A., & Palpaceli, M.C. (2024). Developing a YOLOv3-Based Neural Network to Enable Digital Cameras to Detect Features on the Reflective Surface of Objects in Robotic Applications: A Low-Computational Approach with a New Public Dataset. Preprints. https://doi.org/10.20944/preprints202403.0221.v1
Chicago/Turabian Style
Bajrami, A. and Matteo Claudio Palpaceli. 2024 "Developing a YOLOv3-Based Neural Network to Enable Digital Cameras to Detect Features on the Reflective Surface of Objects in Robotic Applications: A Low-Computational Approach with a New Public Dataset" Preprints. https://doi.org/10.20944/preprints202403.0221.v1
Abstract
This study explores the integration of Artificial Intelligence (AI) and collaborative robotics in industrial surface finishing, focusing on feature detection on object reflective surfaces using neural networks in computationally constrained environments. Central to this investigation is the utilization of cameras considered as sensors that can capture detailed images of reflective surfaces. These images are then analyzed by a custom neural network, demonstrating the feasibility of feature detection with non-powerful computers. The public dataset for recognizing light reflection defects, available in the SPADD repository, supports this approach. Employing open-source tools like Darknet YOLOv3 and Yolo\_mark, this method proves particularly advantageous for small and medium-sized enterprises (SMEs) due to its low cost and maintenance requirements. This research highlights the challenges of AI and collaborative robotics in industrial applications, especially in adapting to varying conditions and the need for continuous training with new data coming from several sensors. Looking ahead, plans are in place to integrate this system with cobot-assisted polishing tasks, using cameras to enhance manufacturing efficiency and quality. This work contributes to the broader understanding of practical applications of AI in industrial automation, surface finishing processes, and the effective use of sensor technology.
Keywords
Feature Detection; Object Reflective Surfaces; YOLOv3 Neural Network; Transfer Learning; Digital Cameras; Collaborative Robots; Machine Learning in Manufacturing; Automation in Polishing; Industrial AI Applications; GPU; CUDA and cuDNN
Subject
Engineering, Mechanical Engineering
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
