Comparison of different ANN techniques for automatic defect detection in X-Ray images

Abstract

X-ray imaging is extensively used in the NDT. In the conventional method, interpretation of the large number of radiographs for defect detection and evaluation is carried out manually by operator or expert, which makes the system subjective. Also interpretation of large number of images is tedious and may lead to misinterpretation. Automation of Non-Destructive evaluation techniques is gaining greater relevance but automatic analysis of X-Ray images is still a complex problem, as the images are noisy, low contrast with a number of artifacts. ANN's are systems which can be trained to analyze input data based on conditions provided to derive required output. This makes the system automatic reducing the subjective interference in analysis of data. Artificial neural network based systems are thus a feasible solution to this problem of X-Ray NDT. Due to complex nature of input images and noise present, Noise removal becomes a problem in X-Ray images. Preprocessing techniques based on statistical analysis have shown improvement in image noise reduction. Pixels/group of pixels, which deviate from the general structural pattern and grey scale distribution are located. The statistically processed pixel values are used to obtain the features vector from defective as well as from non-defective areas. Software for pre-processing and analyzing NDT images has been developed. Software allows user to train neural networks for defect detection. Once trained satisfactorily, the software scans the new input image and uses the trained ANN for defect detection. The final image with defect regions marked will be displayed. This system can be used to obtain the probable defective areas in a given input image. This paper presents performance of MLP and RBF for detection of defect. The effect of different types of input viz. template and moments on performance of ANN is discussed.