基于自编码器图像重构的织物瑕疵检测算法

针对含周期图案织物瑕疵检测通常要计算周期,而这又不适应纯色织物,本文提出适应2类织物的检测方法。首先设定检测块大小,按其在无瑕疵图像中随机提取图像块,并训练自编码器。然后将待检图像按设定大小分块,用自编码器重构,并计算重构前后的均方误差。最后对计算结果进行异常值检测,均方误差值偏大的为瑕疵块。实验表明,本文算法适应2类织物,容易实现,检测效果较好。

Fabric Defect Detection Based on Image Reconstruction with Auto-encoder

The fabric defect detection with periodic pattern generally needs to calculate period, which is not suitable for pure texture fabric. In this paper, the detection method for two kinds of fabric is proposed. Firstly, the size of detect block is set, the image block is extracted randomly from non-defect images, and then the auto-encoder is trained. Secondly, according to the size, the test images are divided into several blocks, and reconstructed with the auto-encoder, and then the mean square errors are computed between the reconstructed result and original data. Lastly, outliers of computing results of test images are detected. With rather large mean square errors value, the blocks are defect blocks. The experiments show that the proposed method is suitable for two kinds of fabric, the process is more practical and the detection results are better.