基于卷积神经网络的超声红外热图像分类

在超声红外热像技术应用中，从红外热图像来判断被测对象是否含有裂纹，通常需要先基于人工经验，从红外热图像中提取特征再采用某种模式识别方法进行分类，裂纹的识别与定位过程繁琐且识别率较低。为此，提出一种基于卷积神经网络技术的超声红外热图像裂纹检测与识别方法，其特点是可以直接从超声红外图像中学习特征进而实现是否含有裂纹红外热图像的分类。通过实验得到的含裂纹和不含裂纹金属平板试件的红外热图像，建立卷积神经网络模型对图像中是否含有裂纹进行分类，研究结果表明，参数优化后的卷积神经网络模型对超声红外热图像的有无裂纹分类准确率达到98.7%。

Classification of Ultrasonic Infrared Thermal Images Using a Convolutional Neural Network

In the application of ultrasonic infrared thermographic technology, it is usually necessary to extract features from infrared thermographic images based on artificial experience and then adopt a pattern recognition method to classify the cracks. The identification and positioning process of the cracks is complicated, and the recognition rate is low. Therefore, a method of crack detection and recognition in ultrasonic infrared thermal images based on convolutional neural network technology is proposed in this paper. Its feature is that the features can be directly learned from the ultrasonic infrared image to realize the classification of infrared thermal images containing cracks. Thesis through the research experiment of metal plate specimen of the crack in and do not contain infrared thermal images, the convolutional neural network model is established for whether the image contains crack classification, the results show that the parameter optimized convolution neural network model for ultrasonic infrared thermal images of crack classification accuracy rate reached 98.7%.