Publication: Enhanced yolo-based nondestructive inspection of wood defects and grading system
| datacite.subject.fos | oecd::Engineering and technology::Electrical engineering, Electronic engineering, Information engineering | |
| dc.contributor.author | Liew, Pei Yi | |
| dc.date.accessioned | 2025-11-27T08:20:37Z | |
| dc.date.available | 2025-11-27T08:20:37Z | |
| dc.date.issued | 2024-12-01 | |
| dc.description.abstract | Wood defect inspection is crucial for maintaining structural integrity and aesthetics in the construction and furniture industries, reducing wastage, and ensuring product quality. Current machine vision systems using Machine Learning (ML) algorithms require complex preprocessing, while Deep Learning (DL) algorithms face issues such as lack of feature prioritization, fixed receptive fields, and poor aggregation. Therefore, this research develops an efficient defect inspection system using the You Only Look Once version 8 (YOLOv8) algorithm integrated, followed by a wood grading system based on its output. A custom wood defect dataset with cracks, dead knots, pinholes, and undersized defects is prepared and enhanced with data augmentation techniques. The YOLOv8 is modified by integrating a Convolutional Block Attention Module (CBAM_spatial), Reparametrized Convolution with Channel Shuffle and One-Shot Aggregation (RCS-OSA), and the Weighted Intersection over Union (WIoUv3) loss function, achieving 97% precision, 97.3% recall, 98.9% mAP0.5, 83.1% mAP0.5:0.95, and 84.87FPS, outperforming other state-of-the-art algorithms by improving the ability of feature learning and aggregation. Additionally, an automated wood visual grading system is developed, accurately classifying wood samples into Grades A, B, and C, achieving 93% accuracy, 7.11% Mean Absolute Percentage Error (MAPE) and 89.79% average Intersection over Union (IoU). This research provides a new strategy to enhance wood defect detection and grading efficiency and precision, significantly improving operational effectiveness and product quality in the wood industry. | |
| dc.identifier.uri | https://erepo.usm.my/handle/123456789/23250 | |
| dc.language.iso | en | |
| dc.title | Enhanced yolo-based nondestructive inspection of wood defects and grading system | |
| dc.type | Resource Types::text::thesis::master thesis | |
| dspace.entity.type | Publication | |
| oairecerif.author.affiliation | Universiti Sains Malaysia |