Open AccessBridge defect detection technology based on machine vision and embedded
Author(s) -
Yufei Xie,
Ming Sheng
Publication year - 2021
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1856/1/012061
Subject(s) - computer science , artificial intelligence , convolutional neural network , bridge (graph theory) , computer vision , noise (video) , preprocessor , segmentation , grayscale , image processing , image segmentation , edge detection , drone , enhanced data rates for gsm evolution , pixel , image (mathematics) , medicine , biology , genetics
In order to improve the degree of intelligence of bridge body maintenance, reduce the consumption of manpower and material resources, and improve inspection efficiency, the technology of machine vision inspection is proposed, and a method of detecting bridge body surface defects and embedded design image transmission and positioning is studied. This paper introduced the existing detection methods and technologies of bridge cracks, and analyzed the detection methods of bridge cracks based on image processing and convolutional neural networks. On the basis of this research, the project collected information and processed and analyzed the cracks on the actual bridge deck, and obtained the image preprocessing results of grayscale, removed background noise, thresholded segmentation, and retained edge information and high-frequency signals. And further, the project used drones to capture image data and used deep learning algorithms to achieve automatic detection of aerial crack images. This scheme can effectively reduce the impact of road background and noise on the detection of bridge surface defects.