An Efficient Convolutional Neural Network Model Based on Object-Level Attention Mechanism for Casting Defect Detection on Radiography Images

Abstract

Automatic detection of casting defects on radiography images is an important technology to automatize digital radiography defect inspection. Traditionally, in an industrial application, conventional methods are inefficient when the detection targets are small, local, and subtle in the complex scenario. Meanwhile, the outperformance of deep learning models, such as the convolutional neural network (CNN), is limited by a huge volume of data with precise annotations. To overcome these challenges, an efficient CNN model, only trained with image-level labels, is first proposed for detection of tiny casting defects in a complicated industrial scene. Then, in this article, we present a novel training strategy which can form a new object-level attention mechanism for the model during the training phase, and bilinear pooling is utilized to improve the model capability of detecting local contrast casting defects. Moreover, to enhance the interpretability, we extend class activation maps (CAM) to bilinear CAM (Bi-CAM) which is adapted to bilinear architectures as a visualization technique to reason about the model output. Experimental results show that the proposed model achieves superior performance in terms of each quantitative metric and is suitable for most actual applications. The real-time defect detection of castings is efficiently implemented in the complex scenario.