A target detection method and readable storage medium based on combination of feature vertices
A target detection and vertex technology, applied in the direction of neural learning methods, instruments, biological neural network models, etc., can solve problems such as poor object effects, achieve the effect of reducing the amount of calculation and improving operating efficiency
Active Publication Date: 2022-06-03
SHENZHEN HUAHAN WEIYE TECH
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[0005] This application provides a target detection method based on feature vertex combination and a readable storage medium to solve the problem that existing target detection methods are not effective in identifying occluded objects and objects with extreme aspect ratios
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[0077] The present invention will be further described in detail below through specific embodiments in conjunction with the accompanying drawings. Wherein, similar elements in different implementations adopt associated similar element numbers. In the following implementation manners, many details are described for better understanding of the present application. However, those skilled in the art can readily recognize that some of the features can be omitted in different situations, or can be replaced by other elements, materials, and methods. In some cases, some operations related to the application are not shown or described in the description, this is to avoid the core part of the application being overwhelmed by too many descriptions, and for those skilled in the art, it is necessary to describe these operations in detail Relevant operations are not necessary, and they can fully understand the relevant operations according to the description in the specification and genera...
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A target detection method and readable storage medium based on combination of feature vertices, wherein the method inputs the image to be detected into a preset first detection network to obtain information of feature vertices, and selects at least one hidden layer output from the first detection network The feature map of the feature map and the information of the feature vertices construct the second feature vector together, and input the second feature vector into the preset second detection network to obtain the category information of the feature vertices and the correction amount of the attitude information, and use the correction amount of the attitude information to correct the feature The vertices are corrected for posture, and the feature vertices are combined into the first matching result according to the category information, and the matching score of the first matching result relative to the matching template is calculated to obtain the second matching result, and the geometric transformation relationship between the second matching result and the matching template is supplemented. All the missing feature vertices and connection relationships are used to identify the target object. For occluded objects and objects with extreme aspect ratios, this method has good detection accuracy and high efficiency.
Description
technical field [0001] The invention relates to the technical field of machine vision, in particular to a target detection method based on feature vertex combinations and a readable storage medium. Background technique [0002] In recent years, artificial intelligence and big data have become the focus of attention in various fields at home and abroad. In the field of computer vision, image processing algorithms based on deep learning are widely used. The convolutional neural network is trained by using the image and the one-to-one label information corresponding to the image. After the training, the convolutional neural network can complete image classification, target detection and semantic segmentation. Among them, the target detection convolutional neural network (hereinafter referred to as the target detection network) has many applications in the industry, such as identifying and counting products on the assembly line. The existing target detection networks based on ...
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IPC IPC(8): G06V10/75G06K9/62G06N3/04G06N3/08
CPCG06N3/084G06N3/045
Inventor 李杰明杨洋
Owner SHENZHEN HUAHAN WEIYE TECH