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Corn breakage rate detection method based on GPU embedded platform

A detection method and damage detection technology, applied in image data processing, instrumentation, computing, etc., can solve problems such as time-consuming and inability to apply, and achieve the effect of avoiding manual feature extraction, improving classification efficiency, and reducing the required time.

Inactive Publication Date: 2017-12-26
UNIV OF SCI & TECH OF CHINA
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AI Technical Summary

Problems solved by technology

However, this method is time-consuming, and the processing time of a picture is 72s, so it cannot be applied to the actual production with strict time requirements.

Method used

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  • Corn breakage rate detection method based on GPU embedded platform
  • Corn breakage rate detection method based on GPU embedded platform
  • Corn breakage rate detection method based on GPU embedded platform

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Experimental program
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Embodiment Construction

[0030] The present invention will be further described below in conjunction with the accompanying drawings and specific embodiments.

[0031] A kind of corn breakage rate detection method based on GPU embedded platform of the present invention, the execution flow of this method is as follows figure 1 Shown:

[0032] Step 1): Generate a classification model.

[0033] Step 2): Input the RGB image through the CCD camera and perform image segmentation to extract individual corn kernel images.

[0034] Step 3): Use the classification model generated in Step 1 to classify the images in Step 2).

[0035] Step 4): According to the classification results of Step 4, count the number of damaged and intact ones and calculate the damage rate.

[0036] details as follows:

[0037] 1. Training network model

[0038] The present invention adopts GoogLeNet model to carry out finetune, trains and generates the classification model of corn damage detection. First of all, we need to collect...

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PUM

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Abstract

The invention discloses a corn breakage rate detection method based on a GPU embedded platform. The method comprises three portions. A first portion is classification model generation. A second portion is image segmentation, which contains that adhesive corn grain images are segmented so as to extract individual grain images and parallel processing is performed on a segmentation algorithm. A third portion is that a classification model is used to carry out breakage detection on the segmented images and a breakage rate is calculated. In the invention, convex set limit corrosion and condition expansion algorithms are used to effectively solve a segmentation problem of the adhesive corn grain images; a deep learning method is used to avoid a manual characteristic extraction problem and guarantee breakage rate detection accuracy; and the GPU embedded platform is used to carry out parallel processing on a detection algorithm so that the detection algorithm possesses real-time performance.

Description

technical field [0001] The invention relates to the technical fields of image segmentation, deep learning, and parallelization, in particular to a method for detecting corn damage rate based on a GPU embedded platform. Background technique [0002] Corn is the crop with the largest planting area in my country, accounting for more than 35% of the total grain output. It has become the food crop with the largest planting area, the highest yield, and the greatest potential for increasing production in my country. With the development of science and technology, the level of corn mechanized harvesting is increasing year by year. As of 2013, the proportion of corn mechanized harvesting has exceeded 51.57%. For corn mechanical harvesting, failure to detect the harvesting status in time and make corresponding adjustments will lead to high grain breakage rate, unclean grain removal, more grains falling with scattered objects, and other consequences of machine harvesting loss and reduc...

Claims

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Application Information

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IPC IPC(8): G06T7/00G06T7/194G06T5/30
CPCG06T7/0002G06T5/30G06T7/194G06T2207/10004G06T2207/10024G06T2207/20081G06T2207/30188
Inventor 凌强张大勇郑烇王嵩徐骏李峰
Owner UNIV OF SCI & TECH OF CHINA
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