Szczegóły publikacji
Opis bibliograficzny
Development of an object identification algorithm for the forging industry based on standard vision systems / Adrian Litwa, Łukasz MADEJ // Computer Methods in Materials Science : quarterly / Akademia Górniczo-Hutnicza ; ISSN 2720-4081. — Tytuł poprz.: Informatyka w Technologii Materiałów ; ISSN: 1641-3948. — 2024 — vol. 24 no. 1, s. 5–13. — Bibliogr. s. 12–13, Abstr.
Autorzy (2)
Słowa kluczowe
Dane bibliometryczne
| ID BaDAP | 160755 |
|---|---|
| Data dodania do BaDAP | 2025-07-11 |
| Tekst źródłowy | URL |
| DOI | 10.7494/cmms.2024.1.0824 |
| Rok publikacji | 2024 |
| Typ publikacji | artykuł w czasopiśmie |
| Otwarty dostęp |  |
| Creative Commons | |
| Czasopismo/seria | Computer Methods in Materials Science |
Abstract
The work aims to develop an algorithm for identifying objects in a forging plant under production conditions. Particular emphasis is placed on the accurate detection and tracking of forgings that are transferred along the forging line and, if possible, detection will also cover employees controlling and supporting the operation of forging machines, all of this with the use of standard vision systems. An algorithm prepared in such way will allow the performance of effective detections that will support activities related to the control of the movement of forging elements, the analysis of safety in workplaces, and the monitoring of compliance with Occupational Health and Safety Regulations by employees, as well as also allowing for the introduction of additional optimization algorithms that will further enrich the presented model, which may prove to be a long-term goal that will form the basis for subsequent work. Three algorithmic solutions with different levels of complexity were considered during the research. The first two are based on artificial neural network solutions, while the last one utilizes classical image processing algorithms. The datasets for training and validation in the former cases were generated based on the recordings taken from standard cameras located in the forging plant. Data were acquired from three cameras, two of which were used to create training and validation sets, and a third one was used to verify how the developed algorithms would work in a variable environment that was previously unknown to the models. The impact of model parameters on the results is presented at this stage of the research. It has been proven that machine learning-based solutions cope very well with object detection problems and achieve high accuracies after a precise selection of hyperparameters. Algorithms show the performance of detections with excellent accuracy of 92.5% for YOLOv5 and 94.3% for Mask R-CNN. However, a competitive solution using only image transformations without machine learning showed satisfactory results that can also be obtained with simpler approaches.
