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.

翻译：目前,正在对无人驾驶自动化进行研究,以尽量减少再栏生产的损失率和在再栏工厂切割过程中生产有缺陷产品时的校准时间和准确性;在本文中,我们提出一种方法,用以检测和跟踪以YOLO(你只看一次)v3为基础的进入机器视像摄像机的端点图像,并提前预测再栏端点,同时预测获得的坐标的罪状指数回归。拟议方法解决了在OPPIDet(物体定位预测检测)模型中再栏端点距离很远的框架点的高预测误差率问题。