小波分解在带钢缺陷检测中的应用

Application of Wavelet Decomposition in Steel Strip Defect Detection

光照不均会降低带钢图像的质量,在研究带钢缺陷特点的基础上,提出一种新的带钢缺陷检测方法。首先,对图像取对数处理并进行小波分解,其次分别对小波分解的子图进行同态滤波,然后对滤波后的子图进行中央周边差操作形成差分子图,在此基础上,对差分子图进行融合处理并取指数处理得到高对比度的缺陷图像,最后采用Otsu分割方法对缺陷图像分割。实验结果表明,该方法能增强缺陷图像对比度,图像细节部分清晰,同时可抑制噪声的影响,能够有效地实现缺陷图像的分割。

The influence of illumination reduces the quality of strip image , a new kind of steel strip detection method is put for-ward through analysis of strip defect characteristics .First of all, the logarithm of strip image gray value is decomposed into a se-ries of sub-graph through the wavelet transform .Secondly , every sub-graph of wavelet decomposition is treated by homomorphism , and then the center-surround difference operation is used to construct difference sub-map.On this basis, difference sub-maps are selected for image fusion and exponential operation is used to get the high contrast defect image .Finally, steel strip defect is de-tected through the segmentation method of maximum between-cluster variance .The result of experiments shows that this method can enhance the defect image contrast with clear image details , inhibit the effect of noise , and effectively realize the rapid detec-tion of strip defect image .