Szczegóły publikacji
Opis bibliograficzny
Detection of fungal infections on the wood surface using LTM imaging / Andrzej SIOMA, Bartosz LENTY // Applied Sciences (Basel) [Dokument elektroniczny]. — Czasopismo elektroniczne ; ISSN 2076-3417. — 2023 — vol. 13 iss. 1 art. no. 490, s. 1–13. — Wymagania systemowe: Adobe Reader. — Bibliogr. s. 12–13, Abstr. — Publikacja dostępna online od: 2022-12-30
Autorzy (2)
Słowa kluczowe
Dane bibliometryczne
| ID BaDAP | 144628 |
|---|---|
| Data dodania do BaDAP | 2023-01-17 |
| Tekst źródłowy | URL |
| DOI | 10.3390/app13010490 |
| Rok publikacji | 2023 |
| Typ publikacji | artykuł w czasopiśmie |
| Otwarty dostęp |  |
| Creative Commons | |
| Czasopismo/seria | Applied Sciences (Basel) |
Abstract
Wood used in production processes can be infected by various fungi growing on its surface. The presence of fungi on the wood surface results from the method of storage, handling and transport of the wood. However, the presence of fungi on wood carries a high risk to the health of production operators and users. At the same time, it has a negative impact on the quality and durability of manufactured products. Because of the risks indicated, an attempt was made to develop an industrial, automated system for detecting fungal infections. This paper presents a vision method for detecting fungal infections on the wood surface. A description of the vision system using the laser triangulation method (LTM) to build a three-dimensional surface image is shown. The paper consists of an analysis of the imaging resolution and a description of the concept of using laser illuminator power selection for identifying fungal-infested surfaces. Imaging results for the selected wavelength of electromagnetic radiation are presented. Measurements and parameters describing the identified areas are shown. It was found that it is possible to choose imaging method parameters and laser illumination power allowing identification under industrial conditions of a fungus-infected region on a wood surface while using the image to determine product measurement parameters.