基于改进YOLOv3的纹理瓷砖缺陷检测

Defect Detection of Texture Tile Using Improved YOLOv3

针对目前瓷砖缺陷检测算法主要依赖人工设计特征和分类器,实际应用中存在调试困难、鲁棒性不足的问题,提出一种基于改进YOLOv3的纹理瓷砖缺陷检测算法。首先,在Darknet-53前加入卷积自编码器,将瓷砖的弱缺陷重构图像与原输入融合,得到更丰富的输入信息。然后,利用K-means聚类方法计算新的锚框,以获得更适合的锚框。最后,针对小样本问题,利用在公共数据集上预训练好的权重初始化网络,以提高模型收敛性能。实验结果表明,改进后的模型平均准确率提高了5个百分点,基本保持原模型的预测速度,可以有效检出纹理瓷砖的孔洞及划痕缺陷。

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.