Image Processing Based Wood Defect Detection

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dc.authoridhttps://orcid.org/0000-0001-9400-5694
dc.authoridhttps://orcid.org/0000-0002-2854-4005
dc.contributor.authorÖzkan, Merve
dc.contributor.authorÖzcan, Caner
dc.date.accessioned2025-01-07T07:43:41Z
dc.date.available2025-01-07T07:43:41Z
dc.date.issued2024-10-17
dc.departmentLisansüstü Eğitim Enstitüsü, Bilgisayar Mühendisliği Ana Bilim Dalı
dc.departmentFakülteler, Mühendislik Fakültesi, Yazılım Mühendisliği Bölümü
dc.description.abstractDetection of defects in wooden structures in the forestry industry has become a crucial area of research. Existing studies have focused on specific categories of wood defects, failing to provide a comprehensive classification for high-quality wood. Trained human operators currently perform a variety of wood quality in wood processing facilities. However, this human-dependent process leads to time and performance losses and inaccurate type. This study aims to address all these challenges in future intelligent production systems by targeting the detection of the fungus in oak wood, one of the wood defect classes. The algorithm created based on image processing utilizes median filtering, Canny edge detection, and masking technologies using the HSV color space. The algorithm then calculates the fungal area ratio to the wooden piece's surface area on the masked image to reach the final result. While existing studies in the literature are primarily based on deep learning methods, there has been limited focus on fungus detection. The novelty of this study, conducted on oak wood, lies in its use of a specific dataset, fungal detection, and image processing. An algorithm has been developed and presented in the literature that can be used in the software of future intelligent production systems in the forestry industry.
dc.identifier.citationÖzkan, M., Özcan, C. (2025). Image Processing Based Wood Defect Detection. In: Mammadova, G., Aliev, T., Aida-zade, K. (eds) Information Technologies and Their Applications. ITTA 2024. Communications in Computer and Information Science, vol 2226. Springer, Cham. https://doi.org/10.1007/978-3-031-73420-5_24
dc.identifier.doi10.1007/978-3-031-73420-5_24
dc.identifier.endpage297
dc.identifier.isbn9783031734199
dc.identifier.isbn9783031734205
dc.identifier.issn1865-0929
dc.identifier.issn1865-0937
dc.identifier.scopus2-s2.0-85207839646
dc.identifier.scopusqualityQ4
dc.identifier.startpage287
dc.identifier.urihttps://doi.org/10.1007/978-3-031-73420-5_24
dc.identifier.urihttps://hdl.handle.net/20.500.14619/14957
dc.identifier.volume2226 CCIS
dc.indekslendigikaynakWeb of Science
dc.language.isoen
dc.publisherSpringer Nature Switzerland
dc.relation.ispartofCommunications in Computer and Information Science
dc.relation.ispartofInformation Technologies and Their Applications
dc.relation.publicationcategoryKitap Bölümü - Uluslararası
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.subjectCanny Edge Detection
dc.subjectHSV
dc.subjectImage Processing
dc.subjectObject Detection
dc.subjectWood Material
dc.titleImage Processing Based Wood Defect Detection
dc.title.alternative2nd International Conference on Information Technologies and Their Applications, ITTA 2024
dc.typeConference Object

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