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Building construction target detection method based on YOLO neural network

A neural network and target detection technology, which is applied in neural learning methods, biological neural network models, neural architectures, etc., can solve problems such as small scale, and achieve the effects of improving recognition accuracy, expanding search areas, and good recognition effects

Inactive Publication Date: 2020-01-14
XI'AN UNIVERSITY OF ARCHITECTURE AND TECHNOLOGY
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  • Application Information

AI Technical Summary

Problems solved by technology

[0004] The object of the present invention is to provide a building construction target detection method based on the YOLO neural network, to solve the defects that the existing YOLO algorithm cannot quickly and accurately identify the depth and small scale of the target layer in the image of the construction site

Method used

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  • Building construction target detection method based on YOLO neural network
  • Building construction target detection method based on YOLO neural network
  • Building construction target detection method based on YOLO neural network

Examples

Experimental program
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Embodiment

[0060] First, preprocess the original images collected from the construction site, the steps are as follows:

[0061] Step 1: Divide the original image obtained from the construction site into two parts, one part is the training set data used to train the target recognition model based on Darknet-53, and the other part is the test set data used to detect the target based on Darknet-53 Identify the model.

[0062] Step 2: Name the data of the training set with a unified label, starting from 1 and incrementing the label, so that the XML file generated later can be easily distinguished.

[0063] Step 3: Use the LabelImg-master labeling tool to label the image data of the training set one by one. After the labeling is completed, convert the generated XML file into a txt file that the model can read.

[0064] At this point, the preprocessing of the original image data is completed, and the training set is obtained. Next, the training of the target recognition model based on Darkne...

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Abstract

The invention discloses a building construction target detection method based on a YOLO neural network. The method comprises the steps: 1, collecting original image data at a construction site, dividing the collected original image data into a test set and a training set, and carrying out the preprocessing of the training set; step 2, training a Darkne-53-based target recognition model of the YOLOneural network; 3, testing the target recognition model based on Darknet-53 by using the test set to obtain a test result; 4, analyzing a test result obtained in the step 3; and step 5, acquiring animage in the construction site, and detecting the building construction target in the acquired image by using a Darknet-53-based target recognition model. According to the method, the defect that theexisting YOLO algorithm cannot quickly and accurately identify the problems of deep target layer and small scale in the building construction site image is solved.

Description

technical field [0001] The invention relates to the field of deep learning target detection, in particular to a method for detecting building construction targets based on a YOLO neural network. Background technique [0002] With the rapid development of my country's construction industry, prefabricated buildings have gradually begun to play an increasingly important role under the trend of increasingly strict technical and construction period requirements in the construction industry. The target detection of prefabricated buildings will have very important research significance. [0003] As a part of computer vision, target detection technology aims to quickly locate and classify targets in images. The target detection of prefabricated building construction is to locate and classify the building construction to be assembled on the construction site. The existing YOLO algorithm can achieve high recognition efficiency when the image structure is clear, the target size is app...

Claims

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Application Information

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IPC IPC(8): G06K9/00G06K9/62G06N3/04G06N3/08
CPCG06N3/08G06V20/176G06V2201/07G06N3/045G06F18/214G06F18/241G06F18/253
Inventor 张翔姚江涛董丽丽
Owner XI'AN UNIVERSITY OF ARCHITECTURE AND TECHNOLOGY
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