计算机科学
背景(考古学)
人工智能
残余物
架空(工程)
直线(几何图形)
轮廓仪
代表(政治)
点云
相似性(几何)
特征(语言学)
模式识别(心理学)
特征提取
计算机视觉
算法
图像(数学)
表面光洁度
材料科学
数学
政治
古生物学
语言学
哲学
几何学
政治学
法学
复合材料
生物
操作系统
作者
Zhezhuang Xu,Ye Lin,Dan Chen,Meng Yuan,Yuhang Zhu,Zhijie Ai,Yazhou Yuan
标识
DOI:10.1016/j.eswa.2023.122789
摘要
Detecting wood broken defects through machine vision is challenging due to the similar appearance of defect and defect-free regions on images. Laser profilometer is a reasonable solution, nevertheless, imperfect point cloud representation, such as slope profile, incontinuity of tiny defects and similarity between broken defects and sound area, poses obstacles. To overcome these challenges, this study proposes a multi-line detection method based on bidirectional long- and short-term memory network (Bi-LSTM) for real-time wood broken defect detection. The feature that represents the extent of surface damage in line-level is designed by residual extraction and sorting operation. The Bi-LSTM combines adjacent information to exaggerate semantic information of detection line. Context information extracted by Bi-LSTM are concatenated for multi-line detection to reduce computation complexity. Finally, detection results are modified by considering the information of adjacent lines of point cloud. Experimental results show that the proposed method achieves real-time detection with high accuracy.
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