Predicting Rebar Endpoints using Sin Exponential Regression Model
This work addresses accuracy improvements in rebar production automation, but it is incremental as it builds on existing YOLOv3 and OPPDet models.
The paper tackles the problem of high prediction error rates in rebar endpoint detection for automated rebar cutting, achieving a reduction in error rates by 0.23 to 0.52% using a sin exponential regression model.
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.