Grain storage pest detection and identification method based on video analytics and system thereof

Abstract

The invention provides a grain storage pest detection and identification method based on video analytics and a corresponding detection and identification device. The method of the invention comprises the following steps: S1. obtaining the multiple frames of continuous images of a moving grain storage sample to be detected; S2. cutting each frame of image into a grain pest area and a background area; S3. in each frame of cut image, defining a grain pest searching area by taking the grain pest as the center, wherein the grain pest searching area is composed of M*M grain pest areas; S4. based on a preset match criteria, searching an area matched with the pest area of the Nth frame of image in the grain pest searching area of the (N+1)th frame of image, and recording the matching degree value between areas; and S5. identifying the grain pest area of which the accumulated matching degree value exceeds a preset threshold value as the area with grain pests. The method can overcome the defects of false detection and missed detection existing in the existing detection and identification method and can judge grain pest survival and types.

Description

technical field [0001] The invention relates to the technical field of detection, in particular to a method and system for detecting and identifying stored grain pests based on video analysis. Background technique [0002] my country is the largest grain storage country in the world, and the loss of grain stored in the treasury due to the damage of grain insects is as high as more than 1 billion yuan every year. The current detection methods for stored grain pests (grain insects) mainly include near-infrared spectroscopy, X-ray methods, image processing and machine vision methods. Among them, the near-infrared spectroscopy method and the X-ray method identify the types of grain insects by scanning the grains one by one. Although the identification effect is good, the efficiency is too low and the cost of the equipment is high. [0003] The detection and recognition technology of stored grain pests based on image processing and machine vision has become a research hotspot in...

AI Technical Summary

Problems solved by technology

However, there are generally two problems in the existing detection and recognition methods based on image processing and machine vision: first, because a static image needs to be processed, image noise will lead to false detection and missed detection of food insects; second, the existing The method cannot judge whether the food insects are alive or not

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