Granary grain quantity monitoring method and device based on convolutional neural network

Abstract

The invention relates to a granary grain quantity monitoring method and device based on a convolutional neural network. The method includes: acquring grain surface image, inputting the grain surface image into a built intra-warehouse image segmentation model based on a convolutional neural network; identifying the grain surface and a reference line, finally calculating the area between the grain surface and the reference line in the grain surface image, calculating an error with a preset area to obtain an error value, and judging that the grain quantity of the granary is changed if the error value is greater than a set error threshold value. The method is a monitoring method for automatically monitoring grain quantity change by utilizing an image processing technology. The method is high in monitoring precision and reliability, and can effectively promote the intelligent upgrading and reconstruction process of the grain depot. Besides, the method can be combined with a detection methodbased on infrared laser scanning, when grain surface changes are found, the infrared laser scanner can be used for scanning the whole granary, the accurate grain volume is obtained, and therefore theservice life of infrared laser is prolonged.

Description

technical field [0001] The invention relates to a method and device for monitoring grain quantity in a granary based on a convolutional neural network. Background technique [0002] At present, grain stock inspection usually adopts direct measurement method, which is not only time-consuming and labor-intensive, but also easily interfered by subjective factors. In recent years, relevant researchers have proposed some advanced measurement methods to improve the accuracy and intelligence of grain quantity detection, such as grain inventory inspection methods based on pressure sensors, grain inventory inspection methods based on infrared laser scanning, and grain inventory inspection methods based on radar detection. Inventory inspection methods and ultrasonic-based grain inventory inspection methods, etc. Compared with the traditional manual grain inventory inspection method, the above method not only improves the efficiency and detection accuracy of warehouse clearance and in...

AI Technical Summary

Problems solved by technology

[0003] The purpose of the present invention is to provide a method for monitoring grain quantity in granaries based on convolutional neural network, in order to solve the problem of poor reliability of existing grain quantity monitoring methods in granary

The present invention also provides a grain quantity monitoring device for granaries based on a convolutional neural network to solve the problem of poor reliability of the existing grain quantity monitoring methods for granaries

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