Nondestructive Defect Detection in Castings by Using Spatial Attention Bilinear Convolutional Neural Network
Zhenhui Tang, Engang Tian, Yongxiong Wang, Licheng Wang, Taicheng Yang
Abstract
X-ray images of castings are widely used in manufacturing for quality assurance. This article investigates the X-ray-image-based defective detection. The main contributions in this article are twofold: first, a new full-image method is proposed to classify defective castings and nondefective ones; and second, by combining two technologies, spatial attention mechanism and bilinear pooling used in deep convolutional neural networks (CNNs), a new spatial attention bilinear CNN is proposed to enhance the representation power of CNN. To validate the above initiatives, extensive experimental studies have been carried out to show the advantages of the new method over a number of existing ones.