System for Automated Infrared Image Processing Based on Neural Network Technologies

The article describes the main problems that non-destructive control experts encounter in processing and analyzing infrared (IR) images obtained using thermographic cameras: noisiness, information redundancy, excessive data volume, etc. A gradual process of automated digital processing and analysis of images on the basis of a software system is considered, which allows not only overcoming the drawbacks of the raw data described above, but also drawing a conclusion about the state of the object based on the obtained image independently. The ability to make expert decision is provided by a neural network component in a system on the basis of Kohonen network used for cluster image analysis. There are the examples of system operation applied to IR images of the lining of solid domestic waste (SDW) incinerator. The use of such systems allows reducing workload of an expert in processing large volumes of data and reducing the probability of errors due to such effect as vision "blurring".

Development and Application of Infrared Thermography Non-Destructive Testing Techniques

Effective testing of defects in various materials is an important guarantee to ensure its safety performance. Compared with traditional non-destructive testing (NDT) methods, infrared thermography is a new NDT technique which has developed rapidly in recent years. Its core technologies include thermal excitation and infrared image processing. In this paper, several main infrared thermography nondestructive testing techniques are reviewed. Through the analysis and comparison of the detection principle, technical characteristics and data processing methods of these testing methods, the development of the infrared thermography nondestructive testing technique is presented. Moreover, the application and development trend are summarized.