人工智能
计算机科学
聚类分析
灰度级
帧速率
模式识别(心理学)
字错误率
特征(语言学)
计算机视觉
材料科学
图像(数学)
语言学
哲学
作者
Junfeng Jing,Dong Hao Zhuo,Huanhuan Zhang,Yong Liang,Min Zheng
标识
DOI:10.1177/1558925020908268
摘要
To improve the detection rate of defect and the fabric product quality, a higher real-time performance fabric defect detection method based on the improved YOLOv3 model is proposed. There are two key steps: first, on the basis of YOLOv3, the dimension clustering of target frames is carried out by combining the fabric defect size and k-means algorithm to determine the number and size of prior frames. Second, the low-level features are combined with the high-level information, and the YOLO detection layer is added on to the feature maps of different sizes, so that it can be better applied to the defect detection of the gray cloth and the lattice fabric. The error detection rate of the improved network model is less than 5% for both gray cloth and checked cloth. Experimental results show that the proposed method can detect and mark fabric defects more effectively than YOLOv3, and effectively reduce the error detection rate.
科研通智能强力驱动
Strongly Powered by AbleSci AI