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

Abstract

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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“…Object detection algorithms of deep learning could be broadly classified into two types: single-stage and two-stage detection algorithms. Single-stage algorithms, such as the YOLO series [23][24][25][26][27] and SSD [28,29], make direct predictions of the categories and positions of targets. Two-stage algorithms are employed by the R-CNN series [30][31][32][33], which includes R-CNN, Fast R-CNN, Faster R-CNN, and Mask-R-CNN.…”