DEFECT DETECTION USING DEEPLAB V3+ ON INDUSTRIAL RADIOGRAPHY OF MACHINES

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 timeconsuming
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 minimize training 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.