Non-Destructive Defect Detection Using MASK R-CNN on Industrial Radiography of Machines

Abstract

Abstract: Many industrial processes, particularly those requiring casting or welding, rely heavily on quality control. Manual quality control processes, on the other hand, are frequently time-consuming and error-prone. To address the increased demand for high-quality products, sophisticated visual inspection technologies are becoming increasingly important in manufacturing lines. Convolutional Neural Networks have recently demonstrated exceptional performance in image classification and localization tasks. Based on the Mask Region-based CNN architecture, this research proposes a solution for detecting casting errors in X-ray pictures. The suggested defect detection system conducts flaw identification and segmentation on input pictures at the same time, making it appropriate for a variety of defect detection jobs. It is demonstrated that training the network to conduct defect detection and defect instance segmentation at the same time leads in greater defect detection accuracy than training on defect detection alone. Transfer learning is used to minimizetraining data requirements while increasing the trained model's prediction accuracy. More precisely, the model is trained using two huge publically available picture datasets before being fine-tuned using a relatively modest metal casting X-ray dataset.The trained model's accuracy outperforms state-of-the-art performance on the GRIMA database of Xray images (GDXray) Castings dataset and is quick enough to be deployed in production. On the GDXray Welds dataset, the system likewise works well.A variety of in-depth research are being undertaken to investigate how transfer learning, multi-task learning, and multi-class learning affect the trained system's performance.