Laser & Optoelectronics Progress, Volume. 59, Issue 10, 1015006(2022)
Defect Detection of Texture Tile Using Improved YOLOv3
The present tile defect detection algorithms mainly rely on manual design features and classifier. In addition, they face debugging difficulties and insufficient robustness in practical applications. Therefore, we proposed a texture tile defect detection algorithm using the improved YOLOv3 model. First, a convolutional autoencoder was added in front of the Darknet-53; the reconstructed images with weak defects were fused with original images to get richer input information. Further, the K-means clustering method was used to get new and more suitable anchors. Finally, to solve the problem of insufficient samples, we used the weights of a pre-trained model trained on a common data set to initialize the network to improve convergence performance. Results show that the average accuracy of the improved model increased by 5 percent, besides it kept the prediction speed of the original model and could effectively detect texture tile holes and scratches.
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Zehui Li, Xindu Chen, Jiasheng Huang, Lei Wu, Yangqi Lian. Defect Detection of Texture Tile Using Improved YOLOv3[J]. Laser & Optoelectronics Progress, 2022, 59(10): 1015006
Category: Machine Vision
Received: Mar. 17, 2021
Accepted: May. 21, 2021
Published Online: May. 16, 2022
The Author Email: Chen Xindu (chenxindu@gdutedu.com.cn)