Detection of Knots in Oak Wood Planks: Instance versus Semantic segmentation

Urtans, Evalds; Bumanis, Karlis; Vecins, Valters; Ancans, Maris; Andrijanova, Aiga; Upenieks, Marcis Teodors; Volkovs, Kristofers

doi:10.1109/bdai56143.2022.9862633

Cited by 2 publications

(1 citation statement)

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“…But also due to the complexity and diversity of the colors and texture of the surface of wood panels [3], a long time of manual visual identification is likely to lead to subjective errors in judgment and low accuracy. In addition, different workers are affected by factors such as technical level and physical condition, and there is a lack of subjective consistency in the judgment of defects, resulting in unstable product quality [4]. In general, the current efficiency of wood panel inspection is relatively low.…”

Section: Introductionmentioning

confidence: 99%

An efficient and accurate surface defect detection method for quality supervision of wood panels

Yi,

Luo,

et al. 2024

Meas. Sci. Technol.

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The wood panel processing sector is integral to the landscape of industrial manufacturing, and automated detection of wood panel surface defects has become an important guarantee for improving the efficiency and quality of processing production. However, due to the diverse scales and shapes of wood panel surface defects, as well as their complex and varied colors and texture characteristics, the efforts to efficiently and accurately detect surface defects in wood panels through existing methods have fallen short. Therefore, the paper proposes an enhanced YOLOx-tiny deep learning network for wood panel surface defect detection. We introduce new modules MFFM and CFEM, instead of the original SPP and Bottleneck modules to enhance key feature extraction and reduce the number of computational parameters.The experimental results conducted on the self-constructed wood panel surface defects dataset show that the mAP of our proposed method is 95.01%, which is 9.58% higher than the original YOLOx-tiny network model, and the defects recall is 91.46%, which is 13.21% higher compared to the original network. Meanwhile, the method is able to reduce 12.22% of computational parameters and 12.67% of GFLOPs, which effectively improves the efficiency of the detection of surface defects on wood panels. In summary, the proposed intelligent surface defect detection approach for wood panels, which utilizes an enhanced YOLOx-tiny deep learning network, has yielded notable outcomes in enhancing both accuracy and efficiency. This method holds significant practical relevance for the wood panel manufacturing sector, offering the potential to enhance both production efficiency and quality. It also explores the automation and intelligent technology in the process of man-made board processing, which provides a valuable reference for the research in related fields.

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Section: Introductionmentioning

confidence: 99%