Eureka AIR delivers breakthrough ideas for toughest innovation challenges, trusted by R&D personnel around the world.

A Rebar Detection Method Based on Deep Convolutional Neural Network

A neural network and deep convolution technology, applied in the field of steel bar detection based on deep convolutional neural network, can solve the problems of limited accuracy and robustness, difficult to put into practical use, etc., to improve geometric deformation ability and generalization Good, high detection accuracy

Active Publication Date: 2021-08-06
广州市颐创信息科技有限公司
View PDF6 Cites 0 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

This method is limited by the lack of accuracy and robustness, and it is difficult to put it into practical use

Method used

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
View more

Image

Smart Image Click on the blue labels to locate them in the text.
Viewing Examples
Smart Image
  • A Rebar Detection Method Based on Deep Convolutional Neural Network
  • A Rebar Detection Method Based on Deep Convolutional Neural Network
  • A Rebar Detection Method Based on Deep Convolutional Neural Network

Examples

Experimental program
Comparison scheme
Effect test

Embodiment Construction

[0043] The present invention will be further described below in conjunction with specific examples.

[0044] Such as figure 1 As shown, the steel bar detection method based on deep convolutional neural network provided in this embodiment, its specific situation is as follows:

[0045] Step 1: Collect pictures of steel bars in the actual construction site, manually mark them, and divide them into training sets and test sets.

[0046] Step 2, perform data enhancement on the training set, including the following steps:

[0047] Step 2.1, carry out center cropping on the training set, cut off the surrounding edges, and only keep 85% or 90% or 95% of the center of the original image;

[0048] Step 2.2, perform multi-scale scaling on the cropped training set, such as zooming to 1000 pixels, 1400 pixels, 1600 pixels, etc. on the short side;

[0049] Step 2.3, horizontally and vertically flip the multi-scale zoomed image;

[0050] Step 2.4, rotate the multi-scale zoomed picture by...

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to View More

PUM

No PUM Login to View More

Abstract

The invention discloses a steel bar detection method based on a deep convolutional neural network, comprising steps: 1) data acquisition; 2) data processing; 3) model construction and training; 4) model evaluation; 5) model deployment. The present invention applies a target detection algorithm based on a deep convolutional neural network to steel bar detection and counting, and proposes a multi-scale and deformation-tolerant steel bar detection network framework. The framework integrates cascaded R-CNN with better detection performance, feature pyramid network that can effectively solve multi-scale detection problems, more stable group normalization, and deformable convolution that can improve the network's ability to learn spatial geometric deformation, etc. module. Compared with traditional steel bar detection methods, the network framework has higher detection accuracy and better generalization, and can be deployed on remote servers or mobile devices.

Description

technical field [0001] The invention relates to the technical field of computer image processing, in particular to a steel bar detection method based on a deep convolutional neural network. Background technique [0002] Object detection is one of the basic problems in the field of computer vision, and its task is to determine the category, size and location of objects in a given image. As one of the cores of computer vision and image semantic understanding, the development of object detection will help to achieve more complex and higher-level vision tasks, such as semantic segmentation, scene understanding, object tracking, and action recognition. For this reason, target detection has always been an active research field in computer vision, and has extremely high academic research value and industrial application value. [0003] In recent years, with the rapid development of deep learning and the continuous improvement of hardware computing capabilities, convolutional neura...

Claims

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to View More

Application Information

Patent Timeline
no application Login to View More
Patent Type & Authority Patents(China)
IPC IPC(8): G06T7/00G06N3/04G06N3/08
CPCG06T7/0004G06T2207/10004G06T2207/20081G06T2207/20084G06T2207/20132G06T2207/20221G06T2207/30242
Inventor 黄少遇徐雪妙叶超
Owner 广州市颐创信息科技有限公司
Who we serve
  • R&D Engineer
  • R&D Manager
  • IP Professional
Why Eureka
  • Industry Leading Data Capabilities
  • Powerful AI technology
  • Patent DNA Extraction
Social media
Eureka Blog
Learn More
PatSnap group products

Browse by: Latest US Patents, China's latest patents, Technical Efficacy Thesaurus, Application Domain, Technology Topic, Popular Technical Reports.

© 2024 PatSnap. All rights reserved.Legal|Privacy policy|Modern Slavery Act Transparency Statement|Sitemap|About US| Contact US: help@patsnap.com