Accurate and automated detection of surface knots on sawn timbers using YOLO-V5 model

Abstract

Knot detection is a challenging problem for the wood industry. Traditional methodologies depend heavily on the features selected manually and therefore were not always accurate due to the variety of knot appearances. This paper proposes an automated framework for addressing the aforementioned problem by using the state-of-the-art YOLO-v5 (the fifth version of You Only Look Once) detector. The features of surface knots were learned and extracted adaptively, and then the knot defects were identified accurately even though the knots vary in terms of color and texture. The proposed method was compared with YOLO-v3 SPP and Faster R-CNN on two datasets. Experimental results demonstrated that YOLO-v5 model achieved the best performance for detecting surface knot defects. F-Score on Dataset 1 was 91.7% and that of Dataset 2 was up to 97.7%. Moreover, YOLO-v5 has clear advantages in terms of training speed and the size of the weight file. These advantages made YOLO-v5 more suitable for the detection of surface knots on sawn timbers and potential for timber grading.