scholarly journals Automatic Identification and Location of Tunnel Lining Cracks

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Pengyu Wang ◽  
Shuhong Wang ◽  
Alipujiang Jierula
Keyword(s):  
Image Recognition ◽  
Input Image ◽  
Automatic Identification ◽  
Comparison Analysis ◽  
Intelligent Algorithm ◽  
Foreign Countries ◽  
Perception System ◽  
Tunnel Crack ◽  
Surface Image ◽  
Practical Engineering

The lining crack is common for the tunnel in the stage of operation, which has seriously influenced the service life and safety of tunnel engineering. It is a new trend to use computer vision to detect tunnel cracks over the past few years in China and foreign countries. By image processing technology and intelligent algorithm, the computer has a hominine visual perception system which understands, analyzes, and determines input image information, thus recognizing and detecting specific objectives. However, the effect of image recognition for tunnel crack now cannot satisfy the demands of practical engineering. SSD algorithm has been used when analyzing features of lining surface image, while comparison analysis has been made from image recognition results, error rate, and running time. The results indicate that the SSD algorithm can accurately and rapidly detect and mark the position of the tunnel crack. The tunnel information obtained from image recognition is subsequently imported into the team independently developed software GeoSMA-3D, which is useful for determining tunnel grade.

scholarly journals Photo Based Location Information Services Using Encryption Free Framework with Duplicate Checking

2020 ◽  
pp. 141-150
Author(s):  
M. Chandraleka ◽  
D. Anitha
Keyword(s):  
Image Recognition ◽  
Input Image ◽  
Information Services ◽  
Image Feature ◽  
Location Information ◽  
Privacy Issue ◽  
The Public ◽  
Related Information ◽  
Current Location ◽  
Original Feature

In mobile, many applications provide services to the users based on the photos provided by the user.Certain applications, client users take a photo of a certain spot and send it to a server, the server identifies the spot with an image recognizer and returns its related information to the users.It can cause a privacy issue because image recognition results are sometimes privacy sensitive.To overcome the problems of existing approaches, proposed an Encryption-Free framework for Privacy preserving Image Recognition, called Enfpire.InEnfPire, the server cannot identify the client users current location, its candidates can only be presented. In proposed thefeature extraction with CNN algorithm help to collect the unique and accurate features from input image and also used Duplicate Detection process to detect images with same features present within same index.In proposed approach user transform the extracted image feature x into y on the user server and sends it to the public server.With the transformation , the effectiveness of the original feature x is degraded so that the public server cannot uniquely recognize the spot-ID of user from y.It only retrives the relevant spot ID’s.The unique spot ID will identify and information regarding the spot and relevant images will be given to the user.

scholarly journals Research on Calculation Algorithms of Sea Target Velocity and Course for Sea Radar

2018 ◽  
Vol 175 ◽  
pp. 03044
Author(s):  
Xiaobin Lin ◽  
Chengzhi Zhang ◽  
Meng Xie ◽  
Nan Chen
Keyword(s):  
Radar Data ◽  
Target Velocity ◽  
Automatic Identification ◽  
Identification System ◽  
Technical Measure ◽  
Comparison Analysis ◽  
Automatic Identification System ◽  
Calculation Algorithms ◽  
Technical Principles

Sea Radar is an important technical measure for protecting national maritime resources and island sovereignty. The calculation of sea target velocity and course is important to monitor and track sea targets for sea radar, for calculation precision is directly related to the tracking effect of sea target. This paper systematically studies the problem on calculation of sea target velocity and course for sea radar, explicates the relevant technical principles, proposes effective algorithms for calculating sea target velocity and course based on multi-frame accumulative information, and gives a corresponding solution suitable for the scenario of sea target maneuvering. Finally, based on comparison analysis of real radar data and Automatic Identification System (AIS) data, we verify the precision of our algorithms.

Intelligent Algorithm in Automatic Fire Alarm Application

2012 ◽  
Vol 605-607 ◽  
pp. 1851-1854
Author(s):  
Yan Qiu Wang ◽  
Yan Wen Wang ◽  
Chun Mei Pei ◽  
Xiu Qing Yang ◽  
Hai Rong Ye
Keyword(s):  
False Alarm ◽  
False Alarm Rate ◽  
Digital Filter ◽  
Fire Detection ◽  
Intelligent Algorithm ◽  
Fire Alarm ◽  
Rate Analysis ◽  
Practical Engineering ◽  
In Fire ◽  
Detection Signal

Characteristics of fire detection signal are proposed that, in fire case non-fire signals caused by other factors can not be separated from fire signals and in non-fire case non-fire signals may produce changes similar to fire signals. An intelligent algorithm is pointed out to reduce false alarm rate and miss alarm rate, it can improve fire alarm accuracy. The intelligent algorithm includes digital filter, sensitivity autoregulation, drift aotocompensation and rising rate analysis, and it is useful in practical engineering.

scholarly journals Comparative Analysis of Machine Learning Algorithms with and without Feature Extraction

2020 ◽  
Vol 6 (12) ◽  
pp. 235-239
Author(s):  
Vatsal Gupta and Saurabh Gautam
Keyword(s):  
Machine Learning ◽  
Feature Extraction ◽  
Image Recognition ◽  
Input Image ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
K Nearest Neighbors ◽  
Machine Learning Classification ◽  
Security Services ◽  
Computational Resources

Image recognition is one of the core disciplines in Computer Vision. It is one of the most widely researched topics of the last few decades. Many advances in image recognition in the past decade, has made it one of the most efficient and powerful disciplines of all, having its applications in every sector including Finance, Healthcare, Security services, Agriculture and many more. Feature extraction is an integral part of image recognition. It helps in training the model more efficiently and with a higher accuracy, by getting rid of any unwanted or unnecessary features, thus reducing the dimensionality of the input image. This also helps in reducing the computational resources required by the algorithm to train, thus making it affordable for people with low end setups. Here we compare the accuracies of different machine learning classification algorithms, and their training times, with and without using feature Extraction. For the purpose of extracting features, a convolutional neural network was used. The model was trained and tested on the data of 12 classes containing a total of 2,175 images. For comparisons, we chose the Logistic regression, K-Nearest Neighbors Classifier, Random forest Classifier, and Support Vector Machine Classifier.

scholarly journals DEEP LEARNING FOR VESSEL DETECTION AND IDENTIFICATION FROM SPACEBORNE OPTICAL IMAGERY

2021 ◽  
Vol V-3-2021 ◽  
pp. 303-310
Author(s):  
G. Matasci ◽  
J. Plante ◽  
K. Kasa ◽  
P. Mousavi ◽  
A. Stewart ◽  
...  
Keyword(s):  
Deep Learning ◽  
Large Scale ◽  
Input Image ◽  
Automatic Identification ◽  
Identification System ◽  
Optical Remote Sensing ◽  
Detection Model ◽  
Model Based ◽  
Optical Images ◽  
Vessel Detection

Abstract. We present a deep learning-based vessel detection and (re-)identification approach from spaceborne optical images. We introduce these two components as part of a maritime surveillance from space pipeline and present experimental results on challenging real-world maritime datasets derived from WorldView imagery. First, we developed a vessel detection model based on RetinaNet achieving a performance of 0.795 F1-score on a challenging multi-scale dataset. We then collected a large-scale dataset for vessel identification by applying the detection model on 200+ optical images, detecting the vessels therein and assigning them an identity via an Automatic Identification System association framework. A vessel re-identification model based on Twin neural networks has then been trained on this dataset featuring 2500+ unique vessels with multiple repeated occurrences across different acquisitions. The model allows to naturally establish similarities between vessel images. It returns a relevant ranking of candidate vessels from a database when provided an input image for a specific vessel the user might be interested in, with top-1 and top-10 accuracies of 38.7% and 76.5%, respectively. This study demonstrates the potential offered by the latest advances in deep learning and computer vision when applied to optical remote sensing imagery in a maritime context, opening new opportunities for automated vessel monitoring and tracking capabilities from space.

scholarly journals Image Denoising Using Adaptive and Overlapped Average Filtering and Mixed-Pooling Attention Refinement Networks

Mathematics ◽  
2021 ◽  
Vol 9 (10) ◽  
pp. 1130
Author(s):  
Ming-Hao Lin ◽  
Zhi-Xiang Hou ◽  
Kai-Han Cheng ◽  
Chin-Hsien Wu ◽  
Yan-Tsung Peng
Keyword(s):  
Image Analysis ◽  
Object Tracking ◽  
Image Recognition ◽  
State Of The Art ◽  
Input Image ◽  
Portable Devices ◽  
Input And Output ◽  
Noisy Input ◽  
Noise Contamination

Cameras are essential parts of portable devices, such as smartphones and tablets. Most people have a smartphone and can take pictures anywhere and anytime to record their lives. However, these pictures captured by cameras may suffer from noise contamination, causing issues for subsequent image analysis, such as image recognition, object tracking, and classification of an object in the image. This paper develops an effective combinational denoising framework based on the proposed Adaptive and Overlapped Average Filtering (AOAF) and Mixed-pooling Attention Refinement Networks (MARNs). First, we apply AOAF to the noisy input image to obtain a preliminarily denoised result, where noisy pixels are removed and recovered. Next, MARNs take the preliminary result as the input and output a refined image where details and edges are better reconstructed. The experimental results demonstrate that our method performs favorably against state-of-the-art denoising methods.

The Design of Electronic Engineering Budget Software Based on Construction Drawing Recognition Technology

2010 ◽  
Vol 34-35 ◽  
pp. 1258-1262
Author(s):  
Zhi Min Hu ◽  
Jun Tang
Keyword(s):  
Cost Analysis ◽  
Image Recognition ◽  
Average Error ◽  
Engineering Construction ◽  
Software Application ◽  
Electronic Engineering ◽  
Practical Engineering ◽  
Engineering Cost ◽  
Semantic Properties

Electronic engineering budget software provides a powerful tool for the electronic engineering cost analysis, reduces the tedious hand-written budget work of the Engineer. However, to break down the project, and input into project budget software are still time-consuming works. This thesis identifies electronic engineering construction drawings using image recognition technology, automatically calculates the engineer quantities in accordance with the semantic properties of the image pixel, and finally designs electronic engineering budget software based on construction drawing recognition technology. Software application shows that the time of budget book using PRBudget is only 1% of manual preparation, and the average error of calculate is 3.89%, so it can apply to practical engineering.

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