scholarly journals UAV-Driven Structural Crack Detection and Location Determination Using Convolutional Neural Networks

Sensors ◽  
2021 ◽  
Vol 21 (8) ◽  
pp. 2650
Author(s):  
Daegyun Choi ◽  
William Bell ◽  
Donghoon Kim ◽  
Jichul Kim
Keyword(s):  
Visual Information ◽  
Crack Detection ◽  
Image Data ◽  
Image Processing Techniques ◽  
Location Determination ◽  
Aerial Vehicle ◽  
Using Data ◽  
Processing Techniques ◽  
Deep Learning Model ◽  
Structural Cracks

Structural cracks are a vital feature in evaluating the health of aging structures. Inspectors regularly monitor structures’ health using visual information because early detection of cracks on highly trafficked structures is critical for maintaining the public’s safety. In this work, a framework for detecting cracks along with their locations is proposed. Image data provided by an unmanned aerial vehicle (UAV) is stitched using image processing techniques to overcome limitations in the resolution of cameras. This stitched image is analyzed to identify cracks using a deep learning model that makes judgements regarding the presence of cracks in the image. Moreover, cracks’ locations are determined using data from UAV sensors. To validate the system, cracks forming on an actual building are captured by a UAV, and these images are analyzed to detect and locate cracks. The proposed framework is proven as an effective way to detect cracks and to represent the cracks’ locations.

Crack Detection on Metal Surfaces Using Image Processing Techniques

2021 ◽  
Author(s):  
Megharaj Sonawane ◽  
Aditya Borse ◽  
Hrishikesh Sonawane ◽  
Aashish Mali ◽  
Prachi Rajarapollu
Keyword(s):  
Image Processing ◽  
Metal Surfaces ◽  
Crack Detection ◽  
Image Processing Techniques ◽  
Processing Techniques

AIRCRAFT CLASSIFICATION USING IMAGE PROCESSING TECHNIQUES AND ARTIFICIAL NEURAL NETWORKS

2011 ◽  
Vol 25 (08) ◽  
pp. 1321-1335 ◽  
Author(s):  
ADIL GURSEL KARACOR ◽  
ERDAL TORUN ◽  
RASIT ABAY
Keyword(s):  
Image Processing ◽  
Image Data ◽  
Radar Data ◽  
Still Image ◽  
Image Processing Techniques ◽  
Black And White ◽  
Rf Signals ◽  
Different Types ◽  
Artificial Neural ◽  
Processing Techniques

Identifying the type of an approaching aircraft, should it be a helicopter, a fighter jet or a passenger plane, is an important task in both military and civilian practices. The task in question is normally done by using radar or RF signals. In this study, we suggest an alternative method that introduces the use of a still image instead of RF or radar data. The image was transformed to a binary black and white image, using a Matlab script which utilizes Image Processing Toolbox commands of Matlab, in order to extract the necessary features. The extracted image data of four different types of aircraft was fed into a three-layered feed forward artificial neural network for classification. Satisfactory results were achieved as the rate of successful classification turned out to be 97% on average.

scholarly journals Devanagari Handwritten Character Recognition Using Deep Learning

2022 ◽  
Vol 10 (1) ◽  
pp. 102-105
Author(s):  
Kannuru Padmaja
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
Character Recognition ◽  
Automation System ◽  
Handwritten Character Recognition ◽  
Image Processing Techniques ◽  
Handwritten Character ◽  
Processing Techniques ◽  
Deep Learning Model

Abstract: In this paper, we present the implementation of Devanagari handwritten character recognition using deep learning. Hand written character recognition gaining more importance due to its major contribution in automation system. Devanagari script is one of various languages script in India. It consists of 12 vowels and 36 consonants. Here we implemented the deep learning model to recognize the characters. The character recognition mainly five steps: pre-processing, segmentation, feature extraction, prediction, post-processing. The model will use convolutional neural network to train the model and image processing techniques to use the character recognition and predict the accuracy of rcognition. Keywords: convolutional neural network, character recognition, Devanagari script, deep learning.

A Study on Visual Cryptography for Colour Images

2017 ◽  
Vol 7 (7) ◽  
pp. 260
Author(s):  
R. Shanmuga Priya ◽  
A. Senthilkumar
Keyword(s):  
Image Processing ◽  
Computational Complexity ◽  
Secret Sharing ◽  
Human Visual System ◽  
Visual Information ◽  
Visual Cryptography ◽  
Pixel Expansion ◽  
Image Processing Techniques ◽  
Printed Text ◽  
Processing Techniques

The intent of this paper is to present some of the major things about visual cryptography for colour images. The idea behind this technique is quite simple and powerful. Visual cryptography deals with visual information like picture, printed text and written notes etc. Visual cryptography also called secret sharing. As the name implies visual cryptography which has a single secret image and more than one shadow images and provided for numerous users. Visual cryptography process depends on various measures such as accuracy, computational complexity, pixel expansion, contrast whether generated it is meaningless or meaningful. Encryption performed by image processing techniques and the decryption carried out by human visual system with the stacking images. Visual cryptography need not require any complicated cryptographic proficiency. So, the intruders or hackers get hard to hack the details programmatically. However, this papers deals with visual cryptography for colour images.

A Study on Various Image Processing Techniques and Hardware Implementation Using Xilinx System Generator

2017 ◽  
pp. 215-230
Author(s):  
Jyotsna Rani ◽  
Ram Kumar ◽  
Abahan Sarkar ◽  
Fazal A. Talukdar
Keyword(s):  
Image Processing ◽  
Hardware Implementation ◽  
Image Data ◽  
Tool Support ◽  
Environment Design ◽  
Image Processing Techniques ◽  
System Generator ◽  
Xilinx System Generator ◽  
Support Software ◽  
Processing Techniques

This article reviews the various image processing techniques in MATLAB and also hardware implementation in FPGA using Xilinx system generator. Image processing can be termed as processing of images using mathematical operations by using various forms of signal processing techniques. The main aim of image processing is to extract important features from an image data and process it in a desired manner and to visually enhance or to statistically evaluate the desired aspect of the image. This article provides an insight into the various approaches of Digital Image processing techniques in Matlab. This article also provides an introduction to FPGA and also a step by step tutorial in handling Xilinx System Generator. The Xilinx System Generator tool is a new application in image processing and offers a friendly environment design for the processing. This tool support software simulation, but the most important is that can synthesize in FPGAs hardware, with the parallelism, robust and speed, this features are essentials in image processing. Implementation of these algorithms on a FPGA is having advantage of using large memory and embedded multipliers. Advances in FPGA technology with the development of sophisticated and efficient tools for modelling, simulation and synthesis have made FPGA a highly useful platform.

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