Version 1
: Received: 24 June 2024 / Approved: 4 July 2024 / Online: 4 July 2024 (06:58:17 CEST)
How to cite:
Greco, D.; Fasihiany, M.; Ranjbar, A. V.; Masulli, F.; Rovetta, S.; Cabri, A. Computational Vision Algorithms on the Edge for Unpalleting System. Preprints2024, 2024070394. https://doi.org/10.20944/preprints202407.0394.v1
Greco, D.; Fasihiany, M.; Ranjbar, A. V.; Masulli, F.; Rovetta, S.; Cabri, A. Computational Vision Algorithms on the Edge for Unpalleting System. Preprints 2024, 2024070394. https://doi.org/10.20944/preprints202407.0394.v1
Greco, D.; Fasihiany, M.; Ranjbar, A. V.; Masulli, F.; Rovetta, S.; Cabri, A. Computational Vision Algorithms on the Edge for Unpalleting System. Preprints2024, 2024070394. https://doi.org/10.20944/preprints202407.0394.v1
APA Style
Greco, D., Fasihiany, M., Ranjbar, A. V., Masulli, F., Rovetta, S., & Cabri, A. (2024). Computational Vision Algorithms on the Edge for Unpalleting System. Preprints. https://doi.org/10.20944/preprints202407.0394.v1
Chicago/Turabian Style
Greco, D., Stefano Rovetta and Alberto Cabri. 2024 "Computational Vision Algorithms on the Edge for Unpalleting System" Preprints. https://doi.org/10.20944/preprints202407.0394.v1
Abstract
This paper presents a comprehensive comparative analysis of four different computational vision algorithms for implementing an unpalleting system in a factory environment, under typical edge computing constraints. The primary objective is to automate the process of detecting and locating specific variable-shaped objects on a pallet, enabling a robotic system to accurately unstack them. The four algorithms evaluated are Pattern Matching, Scale-Invariant Feature Transform, Oriented FAST and Rotated BRIEF, and Haar cascade classifiers. Each technique is described in detail in this paper, and their implementations are outlined. Experimental results are thoroughly analyzed, assessing the algorithms’ performance based on accuracy, robustness to variability, computational speed, detection sensitivity, and resource consumption. The findings reveal the strengths and limitations of each algorithm, providing valuable insights for selecting the most appropriate technique based on the specific requirements of the unpalleting system. This research contributes to the advancement of automated object detection and localization in industrial environments, paving the way for more efficient and reliable unpalleting systems.
Keywords
Unpalleting System; Computer Vision; Object Detection; Pattern Matching; Scale-Invariant Feature Transform (SIFT); Oriented FAST and Rotated BRIEF (ORB); Haar Cascade Classifiers; Industrial Automation; Robotic Manipulators; Machine Vision
Subject
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
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.
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