文献类型：期刊文献

英文题名：Accurate and Automated Detection of Surface Knots on Sawn Timbers Using YOLO-V5 Model

作者：Fang, Yiming[1,2,3];Guo, Xianxin[1];Chen, Kun[1];Zhou, Zhu[2];Ye, Qing[3]

机构：[1]Shaoxing Univ, Sch Mech & Elect Engn, Shaoxing 312000, Peoples R China;[2]Zhejiang A&F Univ, Sch Informat Engn, Hangzhou 311300, Peoples R China;[3]Suncha Bamboo & Wood Technol Co Ltd, Lishui 323899, Peoples R China

年份：2021

卷号：16

期号：3

起止页码：5390

外文期刊名：BIORESOURCES

收录：SCI-EXPANDED(收录号：WOS:000688342600042)、、EI(收录号：20223712729925)、Scopus(收录号：2-s2.0-85124001503)、WOS

基金：The research work presented in this paper was supported by the China Postdoctoral Science Foundation, Grant No. 2019M662112 and the Scientific Research Foundation of Shaoxing University, Grant No. 20195024. The authors gratefully acknowledge the anonymous reviewers for their valuable comments.

语种：英文

外文关键词：Defect detection; Surface knots; Sawn timber; YOLO-v5

外文摘要：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.