Loading Condition Detection of Grain Transportation Vehicle Based on Visual Image

2018 International Conference on Advanced Electronic Materials, Computers and Materials Engineering

材料科学;无线电电子学;计算机科学

Yan, Zhang^1,2 ; Jingtao, Hu^1 ; Xiaoping, Bai^1

Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang, China^1

School of Information and Electronic Engineering, Shandong Technology and Business University, China^2

关键词: Collaborative operations;    Intersection points;    Line detection algorithms;    Monitoring capabilities;    Non-maximal suppressions;    Randomized Hough transform;    Threshold segmentation;    Transportation vehicles;   

Others  :  https://iopscience.iop.org/article/10.1088/1757-899X/439/3/032090/pdf
DOI  :  10.1088/1757-899X/439/3/032090

来源: IOP

【 摘 要 】

To solve problem of grain loading reliably and accurately during master-slave collaborative operation of harvester-grain transportation vehicle structure, the detection methods of loading status based on visual image are proposed in this paper. The original image of grain bin is acquired by in-vehicle. After noise is eliminated by Gaussian filter, edges of binary image are recognized with Sobel operator, and gradient map of grain and bin is obtained by non-maximal-suppression method. Straight segments of grain bin edge are acquired using line detection algorithm of randomized Hough transform, and then region of interest (ROI) is obtained through pairwise intersection points of four straight edges. Grain region is recognized through colour space conversion and threshold segmentation. Loading status of grain bin is identified by the distribution state of grain with the convex points of grain region. The system extracts clear and complete edges of grain bin and monitors grain loading status real-time and dynamically, which avoids grain scattering outside and improves master-slave collaborative operation efficiency and monitoring capability of harvester-grain transportation vehicle.