Radiography inspection (X-ray or gamma ray) is one of the most commonly used
Non-destructive Evaluation (NDE) methods. More and more digital X-ray imaging is used for
medical diagnosis, security screening, or industrial inspection, which is important for
e-manufacturing. In this paper, we firstly introduced an automatic welding defect inspection system
for X-ray image evaluation, defect image database and applications of Artificial Neural Networks
(ANNs) for NDE. Then, feature extraction and selection methods are used for defect representation.
Seven categories of geometric features were defined and selected to represent characteristics of
different kinds of welding defect. Finally, a feed-forward backpropagation neural network is
implemented for the purpose of defect classification. The performance of the proposed methods are
tested and discussed.
Automatic Welding Defect Detection of X-Ray Images by Using Cascade AdaBoost With Penalty Term
