scholarly journals Analisis dan Penerapan Algoritma Convolutional Neural Network untuk Klasifikasi Kendaraan Prioritas

2021 ◽  
Vol 7 (2) ◽  
pp. 135-144
Author(s):  
Rijal Abdulhakim ◽  
Carudin ◽  
Budi Arif Dermawan
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Data Validation ◽  
Testing Accuracy

Jumlah kendaraan bermotor di Indonesia terus menerus meningkat di setiap tahunnya. Hal ini dapat menimbulkan masalah lalu lintas, salah satunya yaitu kemacetan. Dampak yang ditimbulkan dari kemacetan salah satunya yaitu terganggunya arus lalu lintas. Sedangkan menurut UU RI Nomor 22 Tahun 2009 tentang lalu lintas dan angkutan jalan pada pasal 134 terdapat 7 kendaraan yang harus mendapatkan prioritas di jalan raya. Karena itu, dalam penelitian ini dilakukan analisis model klasifikasi untuk jenis kendaraan pemadam kebakaran, ambulans / mobil jenazah, dan kendaraan non-prioritas dengan menerapkan algoritma CNN menggunakan data video dari CCTV yang dikelola oleh ATCS Kota Bandung. Pada penelitian ini terdapat 5 skenario berbeda dimana skenario tersebut dibedakan dengan menggunakan metode holdout dalam pembagian data dan evaluasi model. Hasil penelitian ini menunjukan bahwa skenario terbaik terdapat pada skenario 2 dengan data training sebesar 60%, data validation sebesar 20%, dan data testing sebesar 20% berhasil mendapatkan validation accuracy sebesar 66,15% dan testing accuracy sebesar 69,231%.

scholarly journals Classification of Compressed Domain Images Utilizing Open VINO Inference Engine

2019 ◽  
Vol 9 (1) ◽  
pp. 1669-1678
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Computer System ◽  
Inference Engine ◽  
Intermediate Representation ◽  
Compressed Domain ◽  
Jpeg Images ◽  
Testing Accuracy ◽  
Dct Coefficients

This paper provides a platform to investigate and explore method of ‘partial decoding of JPEG images’ for image classification using Convolutional Neural Network (CNN). The inference is targeting to run on computer system with x86 CPU architecture. We aimed to improve the inference speed of classification by just using part of the compressed domain image information for prediction. We will extract and use the ‘Discrete Cosine Transform’ (DCT) coefficients from compressed domain images to train our models. The trained models are then converted into OpenVINO Intermediate Representation (IR) format for optimization. During inference stage, full decoding is not required as our model only need DCT coefficients which are presented in the process of image partial decoding. Our customized DCT model are able to achieve up to 90% validation and testing accuracy with great competence towards the conventional RGB model. We can also obtain up to 2x times inference speed boost while performing inference on CPU in compressed domain compared with spatial domain employing OpenVINO inference engine.

scholarly journals Automatic Rain Detection System Based on Digital Images of CCTV Cameras Using Convolutional Neural Network Method

2021 ◽  
Vol 893 (1) ◽  
pp. 012048
Author(s):  
Arif Luqman Hakim ◽  
Ristiana Dewi
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Digital Images ◽  
Detection System ◽  
Observation Data ◽  
Detection Process ◽  
Weather Information ◽  
Network Method ◽  
Testing Accuracy ◽  
Evaluation Accuracy

Abstract The Meteorology, Climatology and Geophysics Agency (BMKG) has a duty to provide weather information including rainfall. BMKG has several types of rainfall gauges, but these are not evenly distributed across regions. The solution to increase the density of rainfall observations is to use existing sources to obtain weather information. This research uses Closed Circuit Television (CCTV) that is spread across the Jakarta area to produce information on rainy conditions. The method used is the Convolutional Neural Network (CNN). The image from CCTV will be used for the training and testing process, so as to get the best accuracy model. The results of this model will be used for rain detection on CCTV digital images. The rain detection process is carried out automatically and in real time. The results of the rain detection process will be displayed on the map according to the location where the CCTV was installed. This research has succeeded in making a CNN model for rain detection with a training accuracy of 98.8% and a testing accuracy of 96.4%, as well as evaluating the BMKG observation data, so it has an evaluation accuracy of 96.7%.

scholarly journals Hate Speech Classification in Indonesian Language Tweets by Using Convolutional Neural Network

2021 ◽  
Vol 14 (3) ◽  
pp. 225-239
Author(s):  
Dewa Ayu Nadia Taradhita ◽  
I Ketut Gede Darma Putra
Keyword(s):  
Neural Network ◽  
Social Media ◽  
Convolutional Neural Network ◽  
Hate Speech ◽  
Rapid Development ◽  
Term Weighting ◽  
Weighting Method ◽  
Testing Stage ◽  
Testing Accuracy ◽  
Speech Classification

The rapid development of social media, added with the freedom of social media users to express their opinions, has influenced the spread of hate speech aimed at certain groups. Online based hate speech can be identified by the used of derogatory words in social media posts. Various studies on hate speech classification have been done, however, very few researches have been conducted on hate speech classification in the Indonesian language. This paper proposes a convolutional neural network method for classifying hate speech in tweets in the Indonesian language. Datasets for both the training and testing stages were collected from Twitter. The collected tweets were categorized into hate speech and non-hate speech. We used TF-IDF as the term weighting method for feature extraction. The most optimal training accuracy and validation accuracy gained were 90.85% and 88.34% at 45 epochs. For the testing stage, experiments were conducted with different amounts of testing data. The highest testing accuracy was 82.5%, achieved by the dataset with 50 tweets in each category.

scholarly journals Detection and Classification of Canned Packaging Defects Using Convolutional Neural Network

2021 ◽  
Vol 4 (1) ◽  
pp. 1-11
Author(s):  
Rindi Kusumawardani ◽  
Putu Dana Karningsih
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Network Architecture ◽  
Human Error ◽  
Network Models ◽  
Experimental Result ◽  
Defect Classification ◽  
Inspection Process ◽  
Testing Accuracy

Packaging is one of the important aspects of a product’s identity. The good and adorable packaging can increase product competitiveness because it gives a perception to the customers of good quality products. Therefore, a good packaging display is necessary so that packaging quality inspection is very important. Automated defect detection can help to reduce human error in the inspection process. Convolutional Neural Network (CNN) is an approach that can be used to detect and classify a packaging condition. This paper presents an experiment that compares 5 network models, i.e. ShuffleNet, GoogLeNet, ResNet18, ResNet50, and Resnet101, each network given the same parameters. The dataset is an image of cans packaging which is divided into 3 classifications, No Defect, Minor Defect, and Major Defect. The experimental result shows that network architecture models of ResNet50 and ResNet101 provided the best result for cans defect classification than the other network models, with 95,56% for testing accuracy. The five models have the testing accuracy above 90%, so it can be concluded that all network models are ideal for detecting the packaging defect and defect classification for the cans product.

CONVOLUTIONAL NEURAL NETWORK BASED ALGORITHM FOR CECUM ACHIEVEMENT CONFIRMATION

2020 ◽  
Author(s):  
S Kashin ◽  
D Zavyalov ◽  
A Rusakov ◽  
V Khryashchev ◽  
A Lebedev
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network

Non-Blind Image Deconvolution Based on “Ringing” Removal Using Convolutional Neural Network

2020 ◽  
Vol 2020 (10) ◽  
pp. 181-1-181-7
Author(s):  
Takahiro Kudo ◽  
Takanori Fujisawa ◽  
Takuro Yamaguchi ◽  
Masaaki Ikehara
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Network Architecture ◽  
Large Scale ◽  
Blind Deconvolution ◽  
Training Dataset ◽  
Image Deconvolution ◽  
Classic Problem ◽  
Key Points ◽  
Blind Image

Image deconvolution has been an important issue recently. It has two kinds of approaches: non-blind and blind. Non-blind deconvolution is a classic problem of image deblurring, which assumes that the PSF is known and does not change universally in space. Recently, Convolutional Neural Network (CNN) has been used for non-blind deconvolution. Though CNNs can deal with complex changes for unknown images, some CNN-based conventional methods can only handle small PSFs and does not consider the use of large PSFs in the real world. In this paper we propose a non-blind deconvolution framework based on a CNN that can remove large scale ringing in a deblurred image. Our method has three key points. The first is that our network architecture is able to preserve both large and small features in the image. The second is that the training dataset is created to preserve the details. The third is that we extend the images to minimize the effects of large ringing on the image borders. In our experiments, we used three kinds of large PSFs and were able to observe high-precision results from our method both quantitatively and qualitatively.

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