A visual image-based detection method and device for grain loading status of a grain truck

Abstract

The invention provides a grain transportation vehicle grain loading state detection method and a device based on a visual image. The method comprises steps: image acquisition is carried out on the grain transportation vehicle through a color digital camera, and a planar color image for a grain box port and the grain is acquired; smoothing, edge, line detection and other filters are used for processing the planar color image, and a complete edge contour of the grain box port is acquired; an image processing method such as color space conversion and threshold segmentation is used for acquiring the area of the grain in the grain box in the image; a two-dimensional convex hull algorithm is used for acquiring convex points representing edges of the grain area, and the linear distances between the convex points and the four edges of the grain box port are calculated respectively; according to the distances measured in real time, the loading state of the grain at each distribution position of the grain box port is judged through a preset threshold, and detection on the grain loading state when a reaper unloads the grain to the grain transportation vehicle can be realized; and finally, the grain state and the corresponding operation instruction are displayed on a detection terminal in real time. The method and the device have the advantages of convenient mounting and maintenance, easy implementation, quick detection speed, high stability and high precision.

Description

technical field [0001] The invention relates to the technical field of detection, in particular to a visual image-based detection method and device for the grain loading state of a grain transport vehicle. Background technique [0002] In order to improve the efficiency and monitoring ability of the combine harvester-grain truck owner-slave cooperative operation in our country, it is necessary to focus on solving the dynamic detection problem of grain loading in the grain tank of the grain truck. In the process of unloading the grain in the grain bin of the harvester to the grain transport vehicle, it is necessary to prevent the grain from being scattered outside the grain tank of the grain transport vehicle, and to ensure that the grain is evenly loaded in the grain tank, which requires real-time detection of grain. The loading state of the box grain is used to realize the dynamic adjustment of the relative position of the harvester and the grain truck. [0003] In the pri...

AI Technical Summary

Problems solved by technology

[0003] In the prior art, the detection of the grain loading in the grain tank mainly includes the following four methods: ①A mechanical sensor is used, which includes a scale body, and the volume of the grain in the grain tank is obtained by measuring the scale, and then according to the density of the grain and the measured However, this method has the disadvantages of complex measurement process and poor measurement accuracy; ②Using an ultrasonic sensor, select two points horizontally at a certain height of the grain in the grain tank, one point is used to emit ultrasonic waves, and the other point is used to receive , to determine whether the grains that change over time have reached a known height, but ultrasonic sensors are generally expensive, have high maintenance costs, and are cumbersome to install; Three-dimensional data measurement on the surface of grains and grains, but the three-dimensional laser scanning sensor has strict requirements on the working environment and higher requirements on operators and maintenance personnel; The model calculates the grain loading situation, but the use of pressure sensors requires the establishment of specific mathematical models for different measured grains, which does not have flexibility and versatility

[0004] In summary, the existing mechanical, acoustic, optical, and electronic sensors generally have defects such as high failure rate, poor stability, and poor resistance to harsh environments.

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