Automatic Detection and Identification of Fasteners with Simple Visual Calibration using Synthetic Data

Sangjun Noh, Seunghyeok Back, Raeyoung Kang, Sungho Shin, Kyoobin Lee

Abstract: In this paper, we present a deep learning-based approach to detect and identify multiple fasteners from various camera poses. To distinguish fasteners of similar size and shape from each other, we propose a part identifier network and simple visual calibration method using a reference image. Though the camera poses changes, the model can infer the actual scale of detected parts by just capturing a reference object at once. Also, we present a synthetic data generation pipeline that adopts domain randomization and can automatically generate a training set for various fastener identification. In the experiment, we evaluated the real-world performance of the fully synthetically trained model and showed that it could be directly applied to real-world part identification. This indicates that our approach has the potential to accelerate the model retraining procedure for various part identification tasks since data acquisition requires almost no cost.