scholarly journals Lychee Fruit Detection Based on Monocular Machine Vision in Orchard Environment

Sensors ◽  
2019 ◽  
Vol 19 (19) ◽  
pp. 4091 ◽  
Author(s):  
Qiwei Guo ◽  
Yayong Chen ◽  
Yu Tang ◽  
Jiajun Zhuang ◽  
Yong He ◽  
...  
Keyword(s):  
Machine Vision ◽  
Detection Method ◽  
Histogram Equalization ◽  
Recall Rate ◽  
Support Vector ◽  
Detection Accuracy ◽  
Foreground Region ◽  
Stepwise Method ◽  
Equivalent Area ◽  
Precision Rate

Due to the change of illumination environment and overlapping conditions caused by the neighboring fruits and other background objects, the simple application of the traditional machine vision method limits the detection accuracy of lychee fruits in natural orchard environments. Therefore, this research presented a detection method based on monocular machine vision to detect lychee fruits growing in overlapped conditions. Specifically, a combination of contrast limited adaptive histogram equalization (CLAHE), red/blue chromatic mapping, Otsu thresholding and morphology operations were adopted to segment the foreground regions of the lychees. A stepwise method was proposed for extracting individual lychee fruit from the lychee foreground region. The first step in this process was based on the relative position relation of the Hough circle and an equivalent area circle (equal to the area of the potential lychee foreground region) and was designed to distinguish lychee fruits growing in isolated or overlapped states. Then, a process based on the three-point definite circle theorem was performed to extract individual lychee fruits from the foreground regions of overlapped lychee fruit clusters. Finally, to enhance the robustness of the detection method, a local binary pattern support vector machine (LBP-SVM) was adopted to filter out the false positive detections generated by background chaff interferences. The performance of the presented method was evaluated using 485 images captured in a natural lychee orchard in Conghua (Area), Guangzhou. The detection results showed that the recall rate was 86.66%, the precision rate was greater than 87% and the F1-score was 87.07%.

scholarly journals A Robust Forgery Detection Method for Copy–Move and Splicing Attacks in Images

Electronics ◽  
2020 ◽  
Vol 9 (9) ◽  
pp. 1500
Author(s):  
Mohammad Manzurul Islam ◽  
Gour Karmakar ◽  
Joarder Kamruzzaman ◽  
Manzur Murshed
Keyword(s):  
Model Building ◽  
Performance Metrics ◽  
Detection Method ◽  
Color Image ◽  
Computational Time ◽  
Support Vector ◽  
Detection Accuracy ◽  
Forgery Detection ◽  
Image Forgery ◽  
The Mean

Internet of Things (IoT) image sensors, social media, and smartphones generate huge volumes of digital images every day. Easy availability and usability of photo editing tools have made forgery attacks, primarily splicing and copy–move attacks, effortless, causing cybercrimes to be on the rise. While several models have been proposed in the literature for detecting these attacks, the robustness of those models has not been investigated when (i) a low number of tampered images are available for model building or (ii) images from IoT sensors are distorted due to image rotation or scaling caused by unwanted or unexpected changes in sensors’ physical set-up. Moreover, further improvement in detection accuracy is needed for real-word security management systems. To address these limitations, in this paper, an innovative image forgery detection method has been proposed based on Discrete Cosine Transformation (DCT) and Local Binary Pattern (LBP) and a new feature extraction method using the mean operator. First, images are divided into non-overlapping fixed size blocks and 2D block DCT is applied to capture changes due to image forgery. Then LBP is applied to the magnitude of the DCT array to enhance forgery artifacts. Finally, the mean value of a particular cell across all LBP blocks is computed, which yields a fixed number of features and presents a more computationally efficient method. Using Support Vector Machine (SVM), the proposed method has been extensively tested on four well known publicly available gray scale and color image forgery datasets, and additionally on an IoT based image forgery dataset that we built. Experimental results reveal the superiority of our proposed method over recent state-of-the-art methods in terms of widely used performance metrics and computational time and demonstrate robustness against low availability of forged training samples.

scholarly journals Fuzzy integral-based multi-sensor fusion for arc detection in the pantograph-catenary system

2016 ◽  
Vol 232 (1) ◽  
pp. 159-170 ◽  
Author(s):  
Ilhan Aydin ◽  
Selahattin B Celebi ◽  
Sami Barmada ◽  
Mauro Tucci
Keyword(s):  
Detection Method ◽  
Electrical Power ◽  
Classifier Fusion ◽  
Support Vector ◽  
Fuzzy Integral ◽  
Detection Accuracy ◽  
Correct Operation ◽  
Railway Traffic ◽  
Individual Classifier ◽  
The Individual

The pantograph-catenary subsystem is a fundamental component of a railway train since it provides the traction electrical power. A bad operating condition or, even worse, a failure can disrupt the railway traffic creating economic damages and, in some cases, serious accidents. Therefore, the correct operation of such subsystems should be ensured in order to have an economically efficient, reliable and safe transportation system. In this study, a new arc detection method was proposed and is based on features from the current and voltage signals collected by the pantograph. A tool named mathematical morphology is applied to voltage and current signals to emphasize the effect of the arc, before applying the fast Fourier transform to obtain the power spectrum. Afterwards, three support vector machine-based classifiers are trained separately to detect the arcs, and a fuzzy integral technique is used to synthesize the results obtained by the individual classifiers, therefore implementing a classifier fusion technique. The experimental results show that the proposed approach is effective for the detection of arcs, and the fusion of classifier has a higher detection accuracy than any individual classifier.

An Advanced Automatic Remote Meter Detection Method Based on Machine Vision

2014 ◽  
Vol 568-570 ◽  
pp. 994-1000
Author(s):  
Han Li ◽  
Shao Jun Liu ◽  
Ku Wang ◽  
Xia Liu
Keyword(s):  
Risk Assessment ◽  
Machine Vision ◽  
Correlation Coefficient ◽  
Detection Method ◽  
Correlation Coefficients ◽  
Histogram Equalization ◽  
Inspection Robot ◽  
Site Monitoring ◽  
Template Image ◽  
Otsu’S Method

There are a great amount of electronic meters equipped in the distribution substations, which were traditionally monitored by operators. On-site monitoring for risk assessment of these meters is very important. In this paper, we presented an advanced machine vision based automatic meter detection method toward the development of an online automatic meter reading intelligent inspection robot in substation. Firstly, the image received from the inspection robot was enhanced using histogram equalization. Then, the image was segmented into two parts based on the threshold obtained by Otsu’s method. Using these two parts, and the whole enhanced image, circular Hough transformation was applied on these three images and detected the circle with highest probability on them. The normalized correlation coefficients were calculated between the corresponding areas of those three circles from three images and the template image of SF6meter. Finally, the circle with highest correlation coefficient, which was higher than a certain threshold, was determined to be the meter. If it is lower than the threshold, the algorithm would decide that no meter was found in the image. The method was tested with 222 images obtained in one substation in Xi’an, Shanxi, China, and an 87.4% accuracy was achieved using these images, which indicated the potential of this method.

scholarly journals Vehicles Detection System at Different Weather Conditions

2021 ◽  
pp. 2040-2052
Author(s):  
Mustafa Najm Abdullah ◽  
Yousra Hussein Ali
Keyword(s):  
Intelligent Transportation Systems ◽  
High Efficiency ◽  
Detection System ◽  
Weather Condition ◽  
Weather Conditions ◽  
Histogram Equalization ◽  
Transportation Systems ◽  
Support Vector ◽  
Detection Accuracy ◽  
Gaussian Blur

The importance of efficient vehicle detection (VD) is increased with the expansion of road networks and the number of vehicles in the Intelligent Transportation Systems (ITS). This paper proposes a system for detecting vehicles at different weather conditions such as sunny, rainy, cloudy and foggy days. The first step to the proposed system implementation is to determine whether the video’s weather condition is normal or abnormal. The Random Forest (RF) weather condition classification was performed in the video while the features were extracted for the first two frames by using the Gray Level Co-occurrence Matrix (GLCM). In this system, the background subtraction was applied by the mixture of Gaussian 2 (MOG 2) then applying a number of pre-processing operations, such as Gaussian blur filter, dilation, erosion, and threshold. The main contribution of this paper is to propose a histogram equalization technique for complex weather conditions instead of a Gaussian blur filter for improving the video clarity, which leads to increase detection accuracy. Based on the previous steps, the system defines each region in the frame expected to contain vehicles. Finally, Support Vector Machine (SVM) classifies the defined regions into a vehicle or not.  As compared to the previous methods, the proposed system showed high efficiency of about 96.4% and ability to detect vehicles at different weather conditions.

scholarly journals Vacant Parking Slot Detection in the Around View Image Based on Deep Learning

Sensors ◽  
2020 ◽  
Vol 20 (7) ◽  
pp. 2138 ◽  
Author(s):  
Wei Li ◽  
Libo Cao ◽  
Lingbo Yan ◽  
Chaohui Li ◽  
Xiexing Feng ◽  
...  
Keyword(s):  
Deep Learning ◽  
Detection Method ◽  
Recall Rate ◽  
Visual Environment ◽  
Convolution Kernel ◽  
Number Of Layers ◽  
Detection Stage ◽  
Geometric Cues ◽  
Precision Rate

Due to the complex visual environment, such as lighting variations, shadows, and limitations of vision, the accuracy of vacant parking slot detection for the park assist system (PAS) with a standalone around view monitor (AVM) needs to be improved. To address this problem, we propose a vacant parking slot detection method based on deep learning, namely VPS-Net. VPS-Net converts the vacant parking slot detection into a two-step problem, including parking slot detection and occupancy classification. In the parking slot detection stage, we propose a parking slot detection method based on YOLOv3, which combines the classification of the parking slot with the localization of marking points so that various parking slots can be directly inferred using geometric cues. In the occupancy classification stage, we design a customized network whose size of convolution kernel and number of layers are adjusted according to the characteristics of the parking slot. Experiments show that VPS-Net can detect various vacant parking slots with a precision rate of 99.63% and a recall rate of 99.31% in the ps2.0 dataset, and has a satisfying generalizability in the PSV dataset. By introducing a multi-object detection network and a classification network, VPS-Net can detect various vacant parking slots robustly.

On-Line Detection Method for Automobile Frame Side Rail Process Holes

2012 ◽  
Vol 616-618 ◽  
pp. 1993-1996
Author(s):  
Yu Zhuo Men ◽  
Hai Bo Yu ◽  
Hua Wang ◽  
Jin Gang Gao ◽  
Xin Pan
Keyword(s):  
Machine Vision ◽  
Detection Method ◽  
Detection System ◽  
Ccd Camera ◽  
Line Detection ◽  
Detection Accuracy ◽  
Side Rail ◽  
On Line ◽  
System Prototype

On-line detection method for automobile frame side rail process holes is proposed in this articled. It is achieved by virtue of machine vision technology detection method. Many images captured by CCD camera are processed and analyzed to finally complete the automatic detection of automobile chassis frame process holes. Machine vision technology is applied to achieve the on-line detection of machining quality of frame side rail mounting holes. The developed detection system prototype has very high detection accuracy.

Automatic annotation method on learners’ opinions in case method discussion

2015 ◽  
Vol 12 (2) ◽  
pp. 90-99 ◽  
Author(s):  
Masaki Samejima ◽  
Daichi Hisakane ◽  
Norihisa Komoda
Keyword(s):  
Recall Rate ◽  
Support Vector ◽  
Case Method ◽  
Automatic Annotation ◽  
Simple Method ◽  
Content Type ◽  
The Past ◽  
Decision Tree Classification ◽  
Problems And Solutions ◽  
Precision Rate

Purpose – The purpose of this paper is to annotate an attribute of a problem, a solution or no annotation on learners’ opinions automatically for supporting the learners’ discussion without a facilitator. The case method aims at discussing problems and solutions in a target case. However, the learners miss discussing some of problems and solutions. Design/methodology/approach – Because opinions about problems and solutions on the same case are similar to each other, the proposed method uses opinions that are correctly annotated in past discussions for annotating an appropriate attribute on each opinion in discussions of the same case. The annotation on each opinion is identified by Support Vector Machine learned with opinions and annotations in the past discussion. Findings – Compared to a simple method that uses decision tree classification, this proposed method improves the recall rate and the precision rate of annotating the attribute by over 10 per cent. The proposed method is effective for automatic annotation. Originality/value – Because the recall rate and the precision rate of annotating an attribute of a problem are over 80 per cent, it is possible to make learners aware of problems that they should discuss. On the other hand, the recall rate and the precision rate of annotating an attribute of a solution are still low. The authors discuss the research issue to improve the rates for automatic annotation.

Eye Detection Using Color, Haar Features, and Efficient Support Vector Machine

2012 ◽  
pp. 286-309
Author(s):  
Shuo Chen ◽  
Chengjun Liu
Keyword(s):  
Support Vector Machine ◽  
Face Recognition ◽  
Detection Method ◽  
Support Vector ◽  
Grand Challenge ◽  
Detection Accuracy ◽  
Eye Detection ◽  
Color Information ◽  
Haar Features ◽  
Improved Performance

Eye detection is an important initial step in an automatic face recognition system. Though numerous eye detection methods have been proposed, many problems still exist, especially in the detection accuracy and efficiency under challenging image conditions. The authors present a novel eye detection method using color information, Haar features, and a new efficient Support Vector Machine (eSVM) in this chapter . In particular, this eye detection method consists of two stages: the eye candidate selection and validation. The selection stage picks up eye candidates over an image through color information, while the validation stage applies 2D Haar wavelet and the eSVM to detect the center of the eye among these candidates. The eSVM is defined on fewer support vectors than the standard SVM, which can achieve faster detection speed and higher or comparable detection accuracy. Experiments on Face Recognition Grand Challenge (FRGC) database show the improved performance over existing methods on both efficiency and accuracy.

Research on the Detection Method of the Malicious Attacks on Campus Network

2014 ◽  
Vol 644-650 ◽  
pp. 3291-3294
Author(s):  
Jing Lei Wang
Keyword(s):  
Support Vector Machine ◽  
Detection Method ◽  
Attack Detection ◽  
Support Vector ◽  
Detection Accuracy ◽  
Campus Network ◽  
Malicious Attacks ◽  
Accuracy Rate ◽  
Malicious Attack ◽  
Conventional Manner

The problem of malicious attacks detection on campus network is studied to improve the accuracy of detection. When detecting malicious attacks on campus network, a conventional manner is usually conducted in malicious attack detection of campus network. If a malicious signature is mutated into a new feature, the conventional detection method cannot recognize the new malicious signature, resulting in a relative low detection accuracy rate of malicious attacks. To avoid these problems, in this paper, the malicious attacks detection method for campus network based on support vector machine algorithm is proposed. The plane of support vector machine classification is constructed, to complete the malicious attacks detection of campus network. Experiments show that this approach can improve the accuracy rate of the malicious attack detection, and achieve satisfactory results.

scholarly journals Real-Time Jellyfish Classification and Detection Based on Improved YOLOv3 Algorithm

Sensors ◽  
2021 ◽  
Vol 21 (23) ◽  
pp. 8160
Author(s):  
Meijing Gao ◽  
Yang Bai ◽  
Zhilong Li ◽  
Shiyu Li ◽  
Bozhi Zhang ◽  
...  
Keyword(s):  
Real Time ◽  
Detection Method ◽  
Histogram Equalization ◽  
Detection Accuracy ◽  
Data Set ◽  
Marine Fishery ◽  
Multi Scale ◽  
Underwater Image ◽  
Neural Network Theory

In recent years, jellyfish outbreaks have frequently occurred in offshore areas worldwide, posing a significant threat to the marine fishery, tourism, coastal industry, and personal safety. Effective monitoring of jellyfish is a vital method to solve the above problems. However, the optical detection method for jellyfish is still in the primary stage. Therefore, this paper studies a jellyfish detection method based on convolution neural network theory and digital image processing technology. This paper studies the underwater image preprocessing algorithm because the quality of underwater images directly affects the detection results. The results show that the image quality is better after applying the three algorithms namely prior defogging, adaptive histogram equalization, and multi-scale retinal enhancement, which is more conducive to detection. We establish a data set containing seven species of jellyfishes and fish. A total of 2141 images are included in the data set. The YOLOv3 algorithm is used to detect jellyfish, and its feature extraction network Darknet53 is optimized to ensure it is conducted in real-time. In addition, we introduce label smoothing and cosine annealing learning rate methods during the training process. The experimental results show that the improved algorithms improve the detection accuracy of jellyfish on the premise of ensuring the detection speed. This paper lays a foundation for the construction of an underwater jellyfish optical imaging real-time monitoring system.

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