A method for detecting corn ear damage

A corn ear and damage detection technology, applied in neural learning methods, image analysis, image enhancement and other directions, can solve problems such as corn ear damage, and achieve the effect of less model parameters, smaller model, and improved generalization ability.

Active Publication Date: 2022-03-18
CHINESE ACAD OF AGRI MECHANIZATION SCI
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  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0008] The technical problem to be solved by the present invention is to provide a method for detecting corn ear damage based on deep learning, which is used to solve the problem of real-time detection of corn ear damage in the complex scene of machine harvesting

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  • A method for detecting corn ear damage
  • A method for detecting corn ear damage
  • A method for detecting corn ear damage

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Embodiment Construction

[0053] Below in conjunction with accompanying drawing, structural principle and working principle of the present invention are specifically described:

[0054] The corn ear damage detection method of the present invention comprises the following steps:

[0055] Step S100, obtain the training set pictures and their labels, and use the labels to mark the training set pictures and real pictures, the training set pictures include synthetic pictures and real pictures, and the labels include target parameters and the category of the target Number, the composite picture is synthesized from the picture taken at the elevator of the corn combine harvester and the picture of the damaged corn ear, and the target parameters include the x and y coordinate pairs of the center point of the damaged corn ear in each of the training set pictures parameters and the width and height parameters of the bounding box, the center point coordinate parameters and the width and height parameters of the bo...

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Abstract

A method for detecting corn ear damage, comprising the steps of: obtaining training set pictures and labels thereof, and using the labels to mark the training set pictures and real pictures, the training set pictures including synthetic pictures and real pictures, the The label includes the target parameters and the number of the category to which the target belongs; modify the network structure, re-cluster and calculate the prior frame, use the labeled training set pictures to train the detection model, and use the labeled real pictures to fine-tune the Parameters of the detection model; and target detection, the trained detection model is used to detect the damage of the corn ear picture, so as to predict the position and quantity of the damaged corn ear in the picture through the algorithm. The invention is a method for detecting corn ear damage based on deep learning, which is used to solve the problem of real-time detection of corn ear damage in a complex scene of machine harvesting.

Description

technical field [0001] The invention relates to a method for detecting corn ears, in particular to a method for detecting damaged corn ears based on deep learning. Background technique [0002] With the improvement of the level of agricultural mechanization in our country, the demand for mechanized harvesting of corn is increasing. At present, corn is mainly harvested by ears. Due to the different maturity of corn when harvested by machine, the straw with high green leaf ratio and high moisture content is easy to cause various mechanical failures, and it will also cause some corn ears to be damaged. Corn ear damage will lead to problems such as a high rate of grain damage and more grain falling with the thrown objects. Therefore, real-time detection of machine-harvested corn ear damage becomes very important. The real-time situation of corn picking is processed by image detection technology and displayed on the screen, which can promptly remind the front-end operator to mak...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06T7/00G06N3/04G06N3/08G06V10/774G06V10/82G06V10/762G06V10/764
CPCG06T7/0002G06T2207/30188G06T2207/20081G06T2207/30168G06N3/045
Inventor 韩科立韩增德
Owner CHINESE ACAD OF AGRI MECHANIZATION SCI
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