  Currently, unmanned automation studies are underway to minimize the loss rate
of rebar production and the time and accuracy of calibration when producing
defective products in the cutting process of processing rebar factories. In
this paper, we propose a method to detect and track rebar endpoint images
entering the machine vision camera based on YOLO (You Only Look Once)v3, and to
predict rebar endpoint in advance with sin exponential regression of acquired
coordinates. The proposed method solves the problem of large prediction error
rates for frame locations where rebar endpoints are far away in OPPDet (Object
Position Prediction Detect) models, which prepredict rebar endpoints with
improved results showing 0.23 to 0.52% less error rates at sin exponential
regression prediction points.
