Grain Truck Loading Status Detection Based on Machine Vision

Abstract

In order to improve the efficiency of the combined harvester-grain truck operation, and realize the non-stop unloading of grain, a machine vision-based method for detecting the loading status of the grain truck is introduced. We propose a edge detection model of the cellular neural network (CNN) using the ant lion optimization algorithm (ALO) to identify the edge of the grain bin, and segment the grain bin area by improved random line detection (RLD) method. The grain area is obtained by HSV color feature transformation and the grain convex hull is obtained by the convex hull algorithm. The distance from the grain hull to the edge of the grain bin is measured in real time, and a threshold is preset to determine the loading status of each part of the grain bin