scholarly journals Sistem Klasifikasi Kendaraan Berbasis Pengolahan Citra Digital dengan Metode Multilayer Perceptron

2017 ◽  
Vol 7 (2) ◽  
pp. 139 ◽  
Author(s):  
Muhammad Irfan ◽  
Bakhtiar Alldino Ardi Sumbodo ◽  
Ika Candradewi
Keyword(s):  
Multilayer Perceptron ◽  
Traffic Monitoring ◽  
Training Data ◽  
Vehicle Classification ◽  
Test Form ◽  
Average Value ◽  
Toll Road ◽  
Haar Cascade ◽  
Two Phases ◽  
Video Sensors

The evolution of video sensors and hardware can be used for developing traffic monitoring system vision based.  It can provide information about vehicle passing by utilizing the camera, so that monitoring can be done automatically. It is needed for the processing systems to provide some information regarding traffic conditions. One such approach is to utilize digital image processing.This research consisted of two phases image processing, namely the process of detection and classification. The process of detection using Haar Cascade Classifier with the training data image form the vehicle and data test form the image state of toll road drawn at random. While, Multilayer Perceptron classification process uses by utilizing the result of the detection process. Vehicle classification is divided into three types, namely car, bus and truck. Then the classification parameters were evaluated by accuracy. The test results vehicle detection indicate the value of accuracy is 92.67. Meanwhile, the classification process is done with phase trial and error to evaluate the parameters that have been determined.  Results of the study show the classification system has an average value of the accuracy is  87.60%.

scholarly journals Vehicle Classification using Haar Cascade Classifier Method in Traffic Surveillance System

2017 ◽  
pp. 57-64 ◽  
Author(s):  
Moch Ilham Ramadhani ◽  
Agus Eko Minarno ◽  
Eko Budi Cahyono
Keyword(s):  
Traffic Engineering ◽  
Learning Algorithm ◽  
Video Data ◽  
Vehicle Classification ◽  
Cascade Classifier ◽  
Average Value ◽  
Video Feed ◽  
Haar Cascade ◽  
Visual Shape ◽  
Cascade Filter

Object detection based on digital image processing on vehicles is very important for establishing monitoring system or as alternative method to collect statistic data to make efficient traffic engineering decision. A vehicle counter program based on traffic video feed for specific type of vehicle using Haar Cascade Classifier was made as the output of this research. Firstly, Haar-like feature was used to present visual shape of vehicle, and AdaBoost machine learning algorithm was also employed to make a strong classifier by combining specific classifier into a cascade filter to quickly remove background regions of an image. At the testing section, the output was tested over 8 realistic video data and achieved high accuracy. The result was set 1 as the biggest value for recall and precision, 0.986 as the average value for recall and 0.978 as the average value for precision.

scholarly journals Influence of Noise-Limited Censored Path Loss on Model Fitting and Path Loss-Based Positioning

Sensors ◽  
2021 ◽  
Vol 21 (3) ◽  
pp. 987
Author(s):  
Aki Karttunen ◽  
Mikko Valkama ◽  
Jukka Talvitie
Keyword(s):  
Model Fitting ◽  
Path Loss ◽  
Threshold Level ◽  
Training Data ◽  
Link Distance ◽  
Strength Based ◽  
Radio Systems ◽  
Two Phases ◽  
Wireless Standards ◽  
Influence Of Noise

Positioning is considered one of the key features in various novel industry verticals in future radio systems. Since path loss (PL) or received signal strength-based measurements are widely available in the majority of wireless standards, PL-based positioning has an important role among positioning technologies. Conventionally, PL-based positioning has two phases—fitting a PL model to training data and positioning based on the link distance estimates. However, in both phases, the maximum measurable PL is limited by measurement noise. Such immeasurable samples are called censored PL data and such noisy data are commonly neglected in both the model fitting and in the positioning phase. In the case of censored PL, the loss is known to be above a known threshold level and that information can be used in model fitting and in the positioning phase. In this paper, we examine and propose how to use censored PL data in PL model-based positioning. Additionally, we demonstrate with several simulations the potential of the proposed approach for considerable improvements in positioning accuracy (23–57%) and improved robustness against PL model fitting errors.

scholarly journals Generalized Zero-Shot Vehicle Detection in Remote Sensing Imagery via Coarse-to-Fine Framework

2019 ◽  
Author(s):  
Hong Chen ◽  
Yongtan Luo ◽  
Liujuan Cao ◽  
Baochang Zhang ◽  
Guodong Guo ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Vehicle Detection ◽  
Training Data ◽  
Coarse Grained ◽  
Vehicle Classification ◽  
Vehicle Localization ◽  
Fine Grained ◽  
Coarse To Fine ◽  
Generalized Zero ◽  
Detection And Recognition

Vehicle detection and recognition in remote sensing images are challenging, especially when only limited training data are available to accommodate various target categories. In this paper, we introduce a novel coarse-to-fine framework, which decomposes vehicle detection into segmentation-based vehicle localization and generalized zero-shot vehicle classification. Particularly, the proposed framework can well handle the problem of generalized zero-shot vehicle detection, which is challenging due to the requirement of recognizing vehicles that are even unseen during training. Specifically, a hierarchical DeepLab v3 model is proposed in the framework, which fully exploits fine-grained features to locate the target on a pixel-wise level, then recognizes vehicles in a coarse-grained manner. Additionally, the hierarchical DeepLab v3 model is beneficially compatible to combine the generalized zero-shot recognition. To the best of our knowledge, there is no publically available dataset to test comparative methods, we therefore construct a new dataset to fill this gap of evaluation. The experimental results show that the proposed framework yields promising results on the imperative yet difficult task of zero-shot vehicle detection and recognition.

scholarly journals Identifikasi Kesegaran Ikan Nila menggunakan Teknik Citra Digital

2020 ◽  
Vol 2 (1) ◽  
pp. 6-10
Author(s):  
Juli Elprida Hutagalung ◽  
Mhd Ihsan Pohan ◽  
Yuli Happy Marpaung
Keyword(s):  
Least Squares ◽  
Least Squares Method ◽  
Image Data ◽  
The Body ◽  
Training Data ◽  
Application System ◽  
Fresh Fish ◽  
Average Value ◽  
Color Changes ◽  
Image Format

Fish contain many nutrients that are very beneficial for the body, but often fish are traded in a state of death as well as being alive. To observe the freshness of tilapia is done by the introduction of color changes that appear on digital images using the least squares method. The purpose of this research is to build an image management application system to detect the freshness of tilapia. The data used are 10 samples of tilapia images which are photographed every 1 hour for 15 hours and obtained 150 image data and then processed and analyzed using the least squares method. The first process begins with image processing by cropping at the edge of the eye of the original image and then proceed with resizing to 1000 x 1000 pixels and changing the image format to *. Png. After the image has been processed then the average value is calculated rata grayscale uses the 'rata_rata Gambar' application system and an equation is stored which is stored as training data on the application system. After the image has been processed then the image is input into the system, the image will be converted into grayscale form and displayed at a predetermined place together with the rgb and grayscale histograms and then calculated using the least squares method. The last process we do is matching the test image with the image stored as training data and we conclude whether the image is (very fresh, fresh, fresh enough, not fresh, or very not fresh), the percentage of freshness of the anchor fish, and the length of time the anchor fish dies. This study used 150 samples of fish images from fresh fish that were still very fresh until the fish were not very fresh (rotten).

scholarly journals Identification of Disease on Leaves Soybean using Modified Otsu and Learning Vector Quantization Neural Networks

Kursor ◽  
2018 ◽  
Vol 9 (3) ◽  
Author(s):  
Candra Dewi ◽  
Muhammad Sa’idul Umam ◽  
Imam Cholissodin
Keyword(s):  
Vector Quantization ◽  
Threshold Value ◽  
Learning Vector Quantization ◽  
Training Data ◽  
Blue Color ◽  
Phakopsora Pachyrhizi ◽  
Average Value ◽  
Maximum Value ◽  
Otsu Method ◽  
Soybean Leaf

Disease of the soybean crop is one of the obstacles to increase soybean production in Indonesia. Some of these diseases usually are found in the leaves and resulted to the crop become unhealthy. This study aims to identify disease on soybean leaf through leaves image by applying the Learning Vector Quantization (LVQ) algorithm. The identification begins with preprocessing using modified Otsu method to get part of the diseases on the leaves with a certain threshold value. The next process is to identify the type of disease using LVQ. This process uses the minimum value, the maximum value and the average value of the red, green and blue color of the image. The testing conducted in this study is to identify two diseases called Peronospora manshurica (Downy Mildew) and phakopsora pachyrhizi (Karat). The result of testing by using 60 training data and the value of all recommendations parameters obtained the highest accuracy of identification is 95% %, but the more stable accuracy is 90%. This result shows that the method perform quite well identification of two mentioned disease.

Forecasting the Crop Yield Production in Trichy District Using Fuzzy C-Means Algorithm and Multilayer Perceptron (MLP)

2020 ◽  
Vol 11 (3) ◽  
pp. 83-98
Author(s):  
Geetha M. C. S. ◽  
Elizabeth Shanthi I.
Keyword(s):  
Data Mining ◽  
Crop Yield ◽  
Multilayer Perceptron ◽  
Training Data ◽  
Yield Data ◽  
Economic Determinants ◽  
Fuzzy C Means ◽  
Yield Production ◽  
Yield Forecast ◽  
Fuzzy C Means Algorithm

The agricultural stock depends upon several factors like biological, seasonal, and economic determinants. The growers sustain a vital loss if they are not capable of predicting the variations in these circumstances. The uncertainty on crop yield can be predicted in a logical and mathematical way. The forecast is made based on the previous archives of yield data secured from that area. Data mining is one such procedure practised to predict the crop yield. The systems examine the data, and on mining, several patterns based on numerous parameters predict the return. This article directs on crop yield forecast in Trichy district by adopting data mining techniques for rule formation on classifying the training data and implementing prediction for test data. The suggested method employs fuzzy C means algorithm for clustering and multilayer perceptron design for prediction. The results of accuracy and execution time of the proposed system correlated with the regression algorithm of prediction.

Investigation of Bonding Materials for Traffic Monitoring Equipment: Properties of Selected Formulated Materials

1996 ◽  
Vol 1536 (1) ◽  
pp. 25-30
Author(s):  
Agata J. Wiackowska-La Rue ◽  
David W. Fowler ◽  
Eric J. Ueber
Keyword(s):  
Laboratory Testing ◽  
Traffic Monitoring ◽  
Vehicle Classification ◽  
Test Results ◽  
Foreseeable Future ◽  
Monitoring Equipment ◽  
Testing Program ◽  
Accurate Knowledge ◽  
New Construction ◽  
Monitoring Devices

Planning for new construction and rehabilitation requires accurate knowledge of traffic volume, vehicle classification, and axle loads. Monitoring and controlling the movements of cars and other vehicles will grow increasingly important in the foreseeable future. To estimate the number of vehicles, truck weights, and speed, piezoelectric axle detectors placed into pavements are used. In Texas several thousand devices at over 1,000 sites are maintained and regularly monitored online. It is essential that the installation of monitoring devices be fast, easy, usable in all types of pavements, and reliable without requiring maintenance or reinstallation for a reasonable length of time. Bonding materials are needed to install the monitoring devices in the pavement. A study was conducted to select materials that would perform satisfactorily. Several chemically different materials were tested. Descriptions of the laboratory testing program, field installations, test results, and conclusions are presented.

Signal-to-Noise Ratio Estimation in Electromyography Signals Contaminated with Electrocardiography Signals

2020 ◽  
Vol 19 (03) ◽  
pp. 2050027
Author(s):  
Thandar Oo ◽  
Pornchai Phukpattaranont
Keyword(s):  
Signal To Noise Ratio ◽  
Training Data ◽  
Signal To Noise ◽  
Zero Crossing ◽  
Ecg Signals ◽  
Average Value ◽  
Emg Signal ◽  
Recognition Systems ◽  
Noise Ratio ◽  
Electromyography Signals

When electromyography (EMG) signals are collected from muscles in the torso, they can be perturbed by the electrocardiography (ECG) signals from heart activity. In this paper, we present a novel signal-to-noise ratio (SNR) estimate for an EMG signal contaminated by an ECG signal. We use six features that are popular in assessing EMG signals, namely skewness, kurtosis, mean average value, waveform length, zero crossing and mean frequency. The features were calculated from the raw EMG signals and the detail coefficients of the discrete stationary wavelet transform. Then, these features are used as inputs to a neural network that outputs the estimate of SNR. While we used simulated EMG signals artificially contaminated with simulated ECG signals as the training data, the testing was done with simulated EMG signals artificially contaminated with real ECG signals. The results showed that the waveform length determined with raw EMG signals was the best feature for estimating SNR. It gave the highest average correlation coefficient of 0.9663. These results suggest that the waveform length could be deployed not only in EMG recognition systems but also in EMG signal quality measurements when the EMG signals are contaminated by ECG interference.

Length-Based Vehicle Classification Schemes and Length Bin Boundaries

2013 ◽  
Vol 2339 (1) ◽  
pp. 19-29 ◽  
Author(s):  
Herbert Weinblatt ◽  
Erik Minge ◽  
Scott Petersen
Keyword(s):  
Rural Areas ◽  
Urban Areas ◽  
The United States ◽  
Traffic Monitoring ◽  
Vehicle Classification ◽  
Pavement Performance ◽  
Department Of Transportation ◽  
Classification Schemes ◽  
Weigh In Motion

Vehicle classification data are an important component of traffic-monitoring programs. Although most vehicle classification conducted in the United States is axle based, some applications could be supplemented or replaced by length-based data. The typically higher deployment cost and reliability issues associated with collecting axle-based data as compared with length-based data present a challenge. This paper reports on analyses of alternative length-based vehicle classification schemes and appropriate length bin boundaries. The primary analyses use data from a set of 13 Long-Term Pavement Performance weigh-in-motion sites, all in rural areas; additional analyses are conducted with data from 11 Michigan Department of Transportation weigh-in-motion sites located in rural and small urban areas and one site located in an urbanized area. For most states, the recommended length-based vehicle classification scheme is a four-bin scheme (motorcycles, short, medium, and long) with an optional very long bin recommended for use by states in which significant numbers of longer combination vehicles operate.

Sign in / Sign up

Export Citation Format

Share Document