支持向量机方法在热波检测图像分割中的应用

作为热波无损检测技术中的关键环节,热波图像分割对结构损伤的有效识别与准确评估具有重要影响。为克服红外热波图像背景噪声大,对比度低等因素对损伤识别的影响,提出了一种基于支持向量机的热波图像分割方法。该方法首先采用Wiener滤波对热波图像进行预处理,然后随机选取目标区域和背景区域内多个像素点的像素值组成目标向量与背景向量,对基于多项式核函数的支持向量机进行训练,最后将训练好的分类器应用于不同的热波图像,实现热波图像的分割。试验结果表明:该方法可有效克服热波图像背景噪声大的问题,较好地保留了缺陷区域分割的完整性;与基于硬阈值的图像分割方法相比,该方法能更好地抑制背景区域的噪声干扰,更有利于损伤的识别与评估。

Application of support vector machine to image segmentation of infrared thermal waving inspection

As a key part of the infrared thermal waving non-destructive testing technique, the thermal wave image segmentation plays an important role in the efficient detection and accurate evaluation of the structural defect. In order to minimize the influence caused by the noisy background and low contrast, the support vector machine was applied to the thermal wave image segmentation. Combining with the Wiener filter, the proposed procedure pre-processed the thermal wave image at first to enhance the contrast. Consequently, several pixel values of the background and target regions were respectively chosen to compose the characteristic vectors and input to the support vector machine, whose kernel function was set to being radial based function. Finally, the classifier obtained by the training step was applied to the thermal wave image and a binary image was obtained, which had been carried out the thermal wave image segmentation. Experimental results show that the proposed method can efficiently enhance the contrast between the background and target regions with a powerful noise retraining capability. Compared with the image segmentation method based on the hard threshold, the proposed procedure is of more benefit to the identification and evaluation of the defects and is valuable for the engineering application.