Synthetic Data-Driven Approach for Missing Nut and Bolt Classification in Flange Joints

Frankly Toro, Hassane Trigui, Yazeed Alnumay, Siddharth Mishra, Sahejad Patel

Abstract: Inspection of bolted flange joints is a routine procedure typically done manually in process-based industries. However, this is a time-consuming task since there are many flanges in a typical operational facility. We present a computer vision-based tool that can be integrated into other systems to enable automated inspection of these flanges. We propose a multi-view image classification architecture for detecting a missing bolt or nut in a flange joint image. To guide the training process, a synthetic dataset with 60,000 image pairs was created to simulate realistic environmental conditions of flange joints. To demonstrate the effectiveness of our approach, an additional real-world dataset of 1,080 flange joint image pairs was manually collected. The proposed approach achieved remarkable performance in classifying missing bolt instances with an accuracy of 95.28% and 95.14% for missing nut instances.