王淑青,顿伟超,黄剑锋,王年涛.基于YOLOv5的瓷砖表面缺陷检测[J].包装工程,2022,43(9):217-224.
WANG Shu-qing,DUN Wei-chao,HUANG Jian-feng,WANG Nian-tao.Ceramic Tile Surface Defect Detection Based on YOLOv5[J].Packaging Engineering,2022,43(9):217-224. |
| 基于YOLOv5的瓷砖表面缺陷检测 |
| Ceramic Tile Surface Defect Detection Based on YOLOv5 |
| 投稿时间：2021-08-20 |
| DOI：10.19554/j.cnki.1001-3563.2022.09.029 |
| 中文关键词: 瓷砖 YOLOv5 深度学习 缺陷检测 |
| 英文关键词: ceramic tile YOLOv5 deep learning defect detection |
| 基金项目:国家自然科学基金青年基金(62006073) |
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| 中文摘要: |
| 目的 针对目前的瓷砖表面人工缺陷检测效率低的问题，提出一种基于深度学习YOLOv5算法实现对生产线瓷砖表面缺陷的检测。方法 首先对数据集进行切图分割与数据增强处理，再通过labelimg对数据集进行数据标注，然后将数据集送入到优化后的YOLOv5网络模型进行迭代训练，并将最优权重用于测试。结果 通过实验对比，YOLOv5模型的检测准确率高于Faster RCNN、SSD、YOLOv4这3种模型，其检测平均准确度高于96%，平均检测时间为14 ms。结论 表明该方法能够检测生产过程中的瓷砖缺陷问题，在瓷砖缺陷检测上有一定的先进性和实用性。 |
| 英文摘要: |
| In response to the low efficiency of artificial defect detection of the ceramic tile surface, this paper proposed a deep-learning YOLOv5 algorithm to detect the defects of the ceramic tile surface at the production line. For a start, figure cutting and segmentation, as well as data enhancement processing were performed against the data set. Then, data in the dataset was labeled through labelimg. In the end, the dataset was sent to the optimized YOLOv5 network model for iterative training, with the optimal weight used in the test. After comparison in the experiment, the detection accuracy of the YOLOv5 model is higher than that of the Faster RCNN, SSD and YOLOv4 model, with average accuracy of over 96% and an average detection time of 14 ms, So the method is advanced and practical in ceramic tile defect detection. The defects of ceramic tiles can be detected during the production process with this method. |
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