Quality control is a fundamental component of many manufacturing processes,
especially those involving casting or welding. However, manual quality control
procedures are often time-consuming and error-prone. In order to meet the
growing demand for high-quality products, the use of intelligent visual
inspection systems is becoming essential in production lines. Recently,
Convolutional Neural Networks (CNNs) have shown outstanding performance in both
image classification and localization tasks. In this article, a system is
proposed for the identification of casting defects in X-ray images, based on
the Mask Region-based CNN architecture. The proposed defect detection system
simultaneously performs defect detection and segmentation on input images,
making it suitable for a range of defect detection tasks. It is shown that
training the network to simultaneously perform defect detection and defect
instance segmentation, results in a higher defect detection accuracy than
training on defect detection alone. Transfer learning is leveraged to reduce
the training data demands and increase the prediction accuracy of the trained
model. More specifically, the model is first trained with two large
openly-available image datasets before finetuning on a relatively small metal
casting X-ray dataset. The accuracy of the trained model exceeds state-of-the
art performance on the GRIMA database of X-ray images (GDXray) Castings dataset
and is fast enough to be used in a production setting. The system also performs
well on the GDXray Welds dataset. A number of in-depth studies are conducted to
explore how transfer learning, multi-task learning, and multi-class learning
influence the performance of the trained system.

提出了一种基于掩膜区域卷积神经网络（Mask Region-based CNN）的系统，用于在 X 射线图像中识别铸造缺陷。该系统同时执行输入图像的缺陷检测和分割，并采用转移学习来减少训练数据需求并提高训练模型的预测准确性。