The Study on High Precision 3D Detection Method of Steel Plate Surface Based on Structured Light

2012 ◽  
Vol 572 ◽  
pp. 338-342 ◽  
Author(s):  
Zhi Guo Liang ◽  
Quan Yang ◽  
Ke Xu ◽  
Fei He ◽  
Xiao Chen Wang ◽  
...  
Keyword(s):  
Surface Quality ◽  
Steel Plate ◽  
Detection Method ◽  
Structured Light ◽  
Detection Methods ◽  
Measurement Technology ◽  
Detection Accuracy ◽  
Plate Surface ◽  
Factors Affecting

Structured light 3D measurement technology with its simple structure, non-contact measurement, fast measurement speed and other advantages, has been widely used. Steel plate surface quality detection is not confined to the two-dimensional feature of gray detection, and local topography measurement for surface quality of steel plate detection becomes increasingly important. In this paper, steel plate surface 3D detection method based on structured light and the factors affecting the measurement accuracy are analyzed. Several effective methods of improving 3D detection accuracy are put forward. Compared with the traditional structured light 3D detection methods, the detection accuracy of new methods is remarkably improved, thus possessing better application values.

scholarly journals Quality Detection System of Transparent Nonel Tubes Based on Image Processing

2015 ◽  
Vol 9 (1) ◽  
pp. 697-702
Author(s):  
Guodong Sun ◽  
Wei Xu ◽  
Lei Peng
Keyword(s):  
Image Processing ◽  
Detection Method ◽  
Detection System ◽  
Recognition Rate ◽  
Human Vision ◽  
Detection Methods ◽  
Detection Accuracy ◽  
External Diameter ◽  
Quality Detection

The traditional quality detection method for transparent Nonel tubes relies on human vision, which is inefficient and susceptible to subjective factors. Especially for Nonel tubes filled with the explosive, missed defects would lead to potential danger in blasting engineering. The factors affecting the quality of Nonel tubes mainly include the uniformity of explosive filling and the external diameter of Nonel tubes. The existing detection methods, such as Scalar method, Analysis method and infrared detection technology, suffer from the following drawbacks: low detection accuracy, low efficiency and limited detection items. A new quality detection system of Nonel tubes has been developed based on machine vision in order to overcome these drawbacks. Firstly the system architecture for quality detection is presented. Then the detection method of explosive dosage and the relevant criteria are proposed based on mapping relationship between the explosive dosage and the gray value in order to detect the excessive explosive faults, insufficient explosive faults and black spots. Finally an algorithm based on image processing is designed to measure the external diameter of Nonel tubes. The experiments and practical operations in several Nonel tube manufacturers have proved the defect recognition rate of proposed system can surpass 95% at the detection speed of 100m/min, and system performance can meet the quality detection requirements of Nonel tubes. Therefore this quality detection method can save human resources and ensure the quality of Nonel tubes.

scholarly journals Are microbial indicators and pathogens correlated? A statistical analysis of 40 years of research

2011 ◽  
Vol 9 (2) ◽  
pp. 265-278 ◽  
Author(s):  
J. Wu ◽  
S. C. Long ◽  
D. Das ◽  
S. M. Dorner
Keyword(s):  
Sample Size ◽  
Detection Method ◽  
Drinking Water Treatment ◽  
Detection Methods ◽  
Indicator Organisms ◽  
Recreational Waters ◽  
Factors Affecting ◽  
Single Indicator ◽  
Independent Indicator

Indicator organisms are used to assess public health risk in recreational waters, to highlight periods of challenge to drinking water treatment plants, and to determine the effectiveness of treatment and the quality of distributed water. However, many have questioned their efficacy for indicating pathogen risk. Five hundred and forty cases representing independent indicator–pathogen correlations were obtained from the literature for the period 1970–2009. The data were analyzed to assess factors affecting correlations using a logistic regression model considering indicator classes, pathogen classes, water types, pathogen sources, sample size, the number of samples with pathogens, the detection method, year of publication and statistical methods. Although no single indicator was identified as the most correlated with pathogens, coliphages, F-specific coliphages, Clostridium perfringens, fecal streptococci and total coliforms were more likely than other indicators to be correlated with pathogens. The most important factors in determining correlations between indicator–pathogen pairs were the sample size and the number of samples positive for pathogens. Pathogen sources, detection methods and other variables have little influence on correlations between indicators and pathogens. Results suggest that much of the controversy with regards to indicator and pathogen correlations is the result of studies with insufficient data for assessing correlations.

scholarly journals Bacterial colonies detecting and counting based on enhanced CNN detection method

2021 ◽  
Vol 233 ◽  
pp. 02012
Author(s):  
Shousheng Liu ◽  
Zhigang Gai ◽  
Xu Chai ◽  
Fengxiang Guo ◽  
Mei Zhang ◽  
...  
Keyword(s):  
Target Detection ◽  
Error Detection ◽  
Detection Rate ◽  
Detection Method ◽  
Difficult Problem ◽  
Detection Methods ◽  
Detection Accuracy ◽  
Detection Model ◽  
Confidence Threshold ◽  
Small Targets

Bacterial colonies detecting and counting is tedious and time-consuming work. Fortunately CNN (convolutional neural network) detection methods are effective for target detection. The bacterial colonies are a kind of small targets, which have been a difficult problem in the field of target detection technology. This paper proposes a small target enhancement detection method based on double CNNs, which can not only improve the detection accuracy, but also maintain the detection speed similar to the general detection model. The detection method uses double CNNs. The first CNN uses SSD_MOBILENET_V1 network with both target positioning and target recognition functions. The candidate targets are screened out with a low confidence threshold, which can ensure no missing detection of small targets. The second CNN obtains candidate target regions according to the first round of detection, intercepts image sub-blocks one by one, uses the MOBILENET_V1 network to filter out targets with a higher confidence threshold, which can ensure good detection of small targets. Through the two-round enhancement detection method has been transplanted to the embedded platform NVIDIA Jetson AGX Xavier, the detection accuracy of small targets is significantly improved, and the target error detection rate and missed detection rate are reduced to less than 1%.

scholarly journals Permission Sensitivity-Based Malicious Application Detection for Android

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Yubo Song ◽  
Yijin Geng ◽  
Junbo Wang ◽  
Shang Gao ◽  
Wei Shi
Keyword(s):  
Detection Method ◽  
Malware Detection ◽  
Feature Selection Method ◽  
Machine Learning Algorithms ◽  
Detection Methods ◽  
Detection Accuracy ◽  
Android Malware ◽  
Android Malware Detection ◽  
Private Data ◽  
Weight Allocation

Since a growing number of malicious applications attempt to steal users’ private data by illegally invoking permissions, application stores have carried out many malware detection methods based on application permissions. However, most of them ignore specific permission combinations and application categories that affect the detection accuracy. The features they extracted are neither representative enough to distinguish benign and malicious applications. For these problems, an Android malware detection method based on permission sensitivity is proposed. First, for each kind of application categories, the permission features and permission combination features are extracted. The sensitive permission feature set corresponding to each category label is then obtained by the feature selection method based on permission sensitivity. In the following step, the permission call situation of the application to be detected is compared with the sensitive permission feature set, and the weight allocation method is used to quantify this information into numerical features. In the proposed method of malicious application detection, three machine-learning algorithms are selected to construct the classifier model and optimize the parameters. Compared with traditional methods, the proposed method consumed 60.94% less time while still achieving high accuracy of up to 92.17%.

Benchmarking Steganalysis

2011 ◽  
pp. 266-290
Author(s):  
Andrew D. Ker
Keyword(s):  
Detection Methods ◽  
Statistical Techniques ◽  
Detection Accuracy ◽  
Performance Differences ◽  
Fair Comparison ◽  
Performance Envelope

This chapter discusses how to evaluate the effectiveness of steganalysis techniques. In the steganalysis literature, numerous different methods are used to measure detection accuracy, with different authors using incompatible benchmarks. Thus it is difficult to make a fair comparison of competing steganalysis methods. This chapter argues that some of the choices for steganalysis benchmarks are demonstrably poor, either in statistical foundation or by over-valuing irrelevant areas of the performance envelope. Good choices of benchmark are highlighted, and simple statistical techniques demonstrated for evaluating the significance of observed performance differences. It is hoped that this chapter will make practitioners and steganalysis researchers better able to evaluate the quality of steganography detection methods.

scholarly journals An Area-Context-Based Credibility Detection for Big Data in IoT

2020 ◽  
Vol 2020 ◽  
pp. 1-12
Author(s):  
Bo Zhao ◽  
Xiang Li ◽  
Jiayue Li ◽  
Jianwen Zou ◽  
Yifan Liu
Keyword(s):  
Big Data ◽  
Data Analysis ◽  
Detection Method ◽  
Big Data Analysis ◽  
Detection Methods ◽  
High Detection ◽  
Data Credibility ◽  
High Level

In order to improve the credibility of big data analysis platform’s results in IoT, it is necessary to improve the quality of IoT data. Many detection methods have been proposed to filter out incredible data, but there are certain deficiencies that performance is not high, detection is not comprehensive, and process is not credible. So this paper proposes an area-context-based credibility detection method for IoT data, which can effectively detect point anomalies, behavioral anomalies, and contextual anomalies. The performance of the context determination and the data credibility detection of the device satisfying the area characteristics is superior to the similar algorithms. As the experiments show, the proposed method can reach a high level of performance with more than 97% in metrics, which can effectively improve the quality of IoT data.

scholarly journals Detecting Shilling Attacks with Automatic Features from Multiple Views

2019 ◽  
Vol 2019 ◽  
pp. 1-13 ◽  
Author(s):  
Yaojun Hao ◽  
Fuzhi Zhang ◽  
Jian Wang ◽  
Qingshan Zhao ◽  
Jianfang Cao
Keyword(s):  
Principal Component Analysis ◽  
Recommender Systems ◽  
Detection Method ◽  
Principal Component ◽  
Component Analysis ◽  
Experimental Results ◽  
Detection Methods ◽  
Detection Accuracy ◽  
Multiple Views ◽  
Fine Grained

Due to the openness of the recommender systems, the attackers are likely to inject a large number of fake profiles to bias the prediction of such systems. The traditional detection methods mainly rely on the artificial features, which are often extracted from one kind of user-generated information. In these methods, fine-grained interactions between users and items cannot be captured comprehensively, leading to the degradation of detection accuracy under various types of attacks. In this paper, we propose an ensemble detection method based on the automatic features extracted from multiple views. Firstly, to collaboratively discover the shilling profiles, the users’ behaviors are analyzed from multiple views including ratings, item popularity, and user-user graph. Secondly, based on the data preprocessed from multiple views, the stacked denoising autoencoders are used to automatically extract user features with different corruption rates. Moreover, the features extracted from multiple views are effectively combined based on principal component analysis. Finally, according to the features extracted with different corruption rates, the weak classifiers are generated and then integrated to detect attacks. The experimental results on the MovieLens, Netflix, and Amazon datasets indicate that the proposed method can effectively detect various attacks.

A Survey on Detecting Methods for Harmonic of Power System

2012 ◽  
Vol 614-615 ◽  
pp. 907-910
Author(s):  
Xue Song Zhou ◽  
Guang Zhu Chen ◽  
You Jie Ma
Keyword(s):  
Power System ◽  
Reactive Power ◽  
Detection Method ◽  
Response Speed ◽  
Development Trend ◽  
Detection Methods ◽  
Detection Accuracy ◽  
Harmonic Detection ◽  
Advantages And Disadvantages ◽  
Power System Harmonics

This paper describes Detecting Methods for Harmonics of power system has been considered one of the serious harms for power system. The research of harmonics has obtained high attention among people. The researches of harmonics detection have many methods, such as Based on Fryze theory of harmonic power detection, Instantaneous reactive power theory detection method, Fourier Transformation harmonic detection method, wavelet transform detection method, neural networks harmonic detection method etc. Aiming at harmonic detection, the different detection methods of power system harmonics are summarized and compared. This paper reviews the existing harmonic detection methods, and discuss their advantages and disadvantages in terms of detection accuracy and response speed, and finally summarizes the development trend of the harmonic detection method.

scholarly journals A supervised method for object-based 3D building change detection on aerial stereo images

2014 ◽  
Vol XL-3 ◽  
pp. 259-264 ◽  
Author(s):  
R. Qin ◽  
A. Gruen
Keyword(s):  
Change Detection ◽  
Current Trend ◽  
Detection Methods ◽  
Support Vector ◽  
Detection Accuracy ◽  
Status Change ◽  
Aerial Photos ◽  
Object Based ◽  
The Status

There is a great demand for studying the changes of buildings over time. The current trend for building change detection combines the orthophoto and DSM (Digital Surface Models). The pixel-based change detection methods are very sensitive to the quality of the images and DSMs, while the object-based methods are more robust towards these problems. In this paper, we propose a supervised method for building change detection. After a segment-based SVM (Support Vector Machine) classification with features extracted from the orthophoto and DSM, we focus on the detection of the building changes of different periods by measuring their height and texture differences, as well as their shapes. A decision tree analysis is used to assess the probability of change for each building segment and the traffic lighting system is used to indicate the status "change", "non-change" and "uncertain change" for building segments. The proposed method is applied to scanned aerial photos of the city of Zurich in 2002 and 2007, and the results have demonstrated that our method is able to achieve high detection accuracy.

Sign in / Sign up

Export Citation Format

Share Document