Real-time Twist Rebar Detection System exploiting GAN-based Data Augmentation technique

Abstract

Currently, AI image analysis research is being conducted on automated cutting, bending, and loading systems, which are the main facilities of rebar processing factories. For automation, various datasets through machine vision cameras are required. However, environmental factors include difficult data collection or high production costs to collect datasets in the production process. To solve these problems, we propose a real-time twist rebar detection system based on GAN (Generative adversarial network), with real rebar datasets collected from 20 rebar videos. In this paper, we generated additional datasets from a deep image generation network and detected rebars' endpoints through YOLO (You Only Look Once) v4, a deep-learning object detection model. In experiments, we generated rebar images corresponding to normal and abnormal, the measured quality between real rebar dataset and generated synthetic rebar dataset by FID (Frechet Inception Distance). As a result, FID measurements showed the normal synthetic rebar dataset 79.363 and the abnormal synthetic rebar dataset 113.973. After that, as a result of training in YOLO v4 by combining the synthetic rebar dataset generated from GAN and the real rebar dataset, we obtained the mean Average Precision (mAP) of 100% and a misdetection rate of 5% compared to the real rebar dataset, the mAP increased by 0.6%, and decreased by 10%. Overall, our results demonstrate a strong effect on rebar twist detection accuracy and misdetection rate. © 2022 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).