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Tytuł:
Zastosowanie sieci konwolucyjnej głębokiego uczenia w detekcji pojazdów
Use of deep learning convolutional network in vehicle detectionmears
Autorzy:
Oszutowska-Mazurek, D. A.
Mazurek, P.
Powiązania:
https://bibliotekanauki.pl/articles/136004.pdf
Data publikacji:
2017
Wydawca:
Wyższa Szkoła Techniczno-Ekonomiczna w Szczecinie
Tematy:
sieć konwolucyjna
uczenie głębokie
detekcja pojazdów
przetwarzanie obrazów
image processing
deep learning
convolutional neural network
vehicle detection
Opis:
Wstęp i cel: Detekcja pojazdów na znaczenie w bezpieczeństwie ruchu drogowego oraz programowaniu pojazdów autonomicznych. Celem pracy jest detekcja pojazdów odróżniająca obrazy pojazdów od innych obrazów nie zawierających pojazdów. Materiał i metody: W pracy wykorzystano bazę pojazdów zawierającą obrazy ekstrahowane z sekwencji wideo, które przetwarzano za pomocą sieci konwolucyjnej głębokiego uczenia. Wyniki: Uzyskana sieć konwolucyjna charakteryzuje się bardzo dobrymi parametrami, krzywa PSNR względem kroku uczenia rośnie co oznacza, że zachodzi proces odszumiania kerneli w całym procesie uczenia. Wniosek: Proponowana metoda może być wykorzystana w programowaniu pojazdów autonomicznych oraz implementacji w Inteligentnych Systemach Transportowych ITS do detekcji pojazdów; bazuje na uczeniu a nie na projektowaniu algorytmu syntetycznego, dzięki temu jest potrzebny relatywnie krótki czas opracowania klasyfikatora.
Introduction and aim: Vehicle detection plays essential role in road safety and automatic vehicle programming. The aim of study is vehicle detection distinguishing car and non-car images Material and methods: Vehicle database images extracted from video sequences were processed by deep learning convolutional network. Results: Obtained convolutional network is characterised by very good parameters, PSNR curve indicates denoising of kernels in learning process. Conclusion: Proposed method is potentially useful in autonomic vehicles programming and Intelligent Transportation Systems (ITS) for vehicles detection. The solution is based on learning, not on synthetic algorithm design, thanks to this, a relatively short time of classifier development is needed.
Źródło:
Problemy Nauk Stosowanych; 2017, 7; 47-56
2300-6110
Pojawia się w:
Problemy Nauk Stosowanych
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Multi-model hybrid ensemble weighted adaptive approach with decision level fusion for personalized affect recognition based on visual cues
Autorzy:
Jadhav, Nagesh
Sugandhi, Rekha
Powiązania:
https://bibliotekanauki.pl/articles/2086876.pdf
Data publikacji:
2021
Wydawca:
Polska Akademia Nauk. Czytelnia Czasopism PAN
Tematy:
deep learning
convolution neural network
emotion recognition
transfer learning
late fusion
uczenie głębokie
konwolucyjna sieć neuronowa
rozpoznawanie emocji
Opis:
In the domain of affective computing different emotional expressions play an important role. To convey the emotional state of human emotions, facial expressions or visual cues are used as an important and primary cue. The facial expressions convey humans affective state more convincingly than any other cues. With the advancement in the deep learning techniques, the convolutional neural network (CNN) can be used to automatically extract the features from the visual cues; however variable sized and biased datasets are a vital challenge to be dealt with as far as implementation of deep models is concerned. Also, the dataset used for training the model plays a significant role in the retrieved results. In this paper, we have proposed a multi-model hybrid ensemble weighted adaptive approach with decision level fusion for personalized affect recognition based on the visual cues. We have used a CNN and pre-trained ResNet-50 model for the transfer learning. VGGFace model’s weights are used to initialize weights of ResNet50 for fine-tuning the model. The proposed system shows significant improvement in test accuracy in affective state recognition compared to the singleton CNN model developed from scratch or transfer learned model. The proposed methodology is validated on The Karolinska Directed Emotional Faces (KDEF) dataset with 77.85% accuracy. The obtained results are promising compared to the existing state of the art methods.
Źródło:
Bulletin of the Polish Academy of Sciences. Technical Sciences; 2021, 69, 6; e138819, 1--11
0239-7528
Pojawia się w:
Bulletin of the Polish Academy of Sciences. Technical Sciences
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Theory I: Deep networks and the curse of dimensionality
Autorzy:
Poggio, T.
Liao, Q.
Powiązania:
https://bibliotekanauki.pl/articles/200623.pdf
Data publikacji:
2018
Wydawca:
Polska Akademia Nauk. Czytelnia Czasopism PAN
Tematy:
deep network
shallow network
convolutional neural network
function approximation
deep learning
sieci neuronowe
aproksymacja funkcji
uczenie głębokie
Opis:
We review recent work characterizing the classes of functions for which deep learning can be exponentially better than shallow learning. Deep convolutional networks are a special case of these conditions, though weight sharing is not the main reason for their exponential advantage.
Źródło:
Bulletin of the Polish Academy of Sciences. Technical Sciences; 2018, 66, 6; 761-773
0239-7528
Pojawia się w:
Bulletin of the Polish Academy of Sciences. Technical Sciences
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Performance analysis of a dual stage deep rain streak removal convolution neural network module with a modified deep residual dense network
Autorzy:
Jayaraman, Thiyagarajan
Chinnusamy, Shankar
Powiązania:
https://bibliotekanauki.pl/articles/2055158.pdf
Data publikacji:
2022
Wydawca:
Uniwersytet Zielonogórski. Oficyna Wydawnicza
Tematy:
single image deraining
deep learning
modified residual dense network
PyTorch
obraz pojedynczy
uczenie głębokie
sieć gęsta
Opis:
The visual appearance of outdoor captured images is affected by various weather conditions, such as rain patterns, haze, fog and snow. The rain pattern creates more degradation in the visual quality of the image due to its physical structure compared with other weather conditions. Also, the rain pattern affects both foreground and background image information. The removal of rain patterns from a single image is a critical process, and more attention is given to remove the structural rain pattern from real-time rain images. In this paper, we analyze the single image deraining problem and present a solution using the dual stage deep rain streak removal convolutional neural network. The proposed single image deraining framework primarily consists of three main blocks: a derain streaks removal CNN (derain SRCNN), a modified residual dense block (MRDB), and a six-stage scale feature aggregation module (3SFAM). The ablation study is conducted to evaluate the performance of various modules available in the proposed deraining network. The robustness of the proposed deraining network is evaluated over the popular synthetic and real-time data sets using four performance metrics such as the peak signal-to-noise ratio (PSNR), the feature similarity index (FSIM), the structural similarity index measure (SSIM), and the universal image quality index (UIQI). The experimental results show that the proposed framework outperforms both synthetic and real-time images compared with other state-of-the-art single image deraining approaches. In addition, the proposed network takes less running and training time.
Źródło:
International Journal of Applied Mathematics and Computer Science; 2022, 32, 1; 111--123
1641-876X
2083-8492
Pojawia się w:
International Journal of Applied Mathematics and Computer Science
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Estimating the distance to an object from grayscale stereo images using deep learning
Autorzy:
Kulawik, Joanna
Powiązania:
https://bibliotekanauki.pl/articles/2202043.pdf
Data publikacji:
2022
Wydawca:
Politechnika Częstochowska. Wydawnictwo Politechniki Częstochowskiej
Tematy:
estimating distance
stereo vision
convolutional neural network
deep learning
szacowanie odległości
widzenie stereoskopowe
konwolucyjne sieci neuronowe
uczenie głębokie
Opis:
This article presents an innovative proposal for estimating the distance between an autonomous vehicle and an object in front of it. Such information can be used, for example, to support the process of controlling an autonomous vehicle. The primary source of information in research is monochrome stereo images. The images were made in compliance with the laws of the canonical order. The developed convolutional neural network model was used for the estimation. A proprietary dataset was developed for the experiments. The analysis was based on the phenomenon of disparity in stereo images. As a result of the research, a correctly trained model of the CNN network was obtained in six variants. High accuracy of distance estimation was achieved. This publication describes an original proposal for a hybrid blend of digital image analysis, stereo-vision, and deep learning for engineering applications.
Źródło:
Journal of Applied Mathematics and Computational Mechanics; 2022, 21, 4; 60--72
2299-9965
Pojawia się w:
Journal of Applied Mathematics and Computational Mechanics
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Vehicle classification using the convolution neural network approach
Autorzy:
Trivedi, Janak
Devi, Mandalapu Sarada
Dhara, Dave
Powiązania:
https://bibliotekanauki.pl/articles/2091225.pdf
Data publikacji:
2021
Wydawca:
Politechnika Śląska. Wydawnictwo Politechniki Śląskiej
Tematy:
convolution neural network
vehicle classification
deep learning
intelligent transportation system
konwolucyjna sieć neuronowa
klasyfikacja pojazdów
uczenie głębokie
inteligentny system transportowy
Opis:
We present vehicle detection classification using the Convolution Neural Network (CNN) of the deep learning approach. The automatic vehicle classification for traffic surveillance video systems is challenging for the Intelligent Transportation System (ITS) to build a smart city. In this article, three different vehicles: bike, car and truck classification are considered for around 3,000 bikes, 6,000 cars, and 2,000 images of trucks. CNN can automatically absorb and extract different vehicle dataset’s different features without a manual selection of features. The accuracy of CNN is measured in terms of the confidence values of the detected object. The highest confidence value is about 0.99 in the case of the bike category vehicle classification. The automatic vehicle classification supports building an electronic toll collection system and identifying emergency vehicles in the traffic.
Źródło:
Zeszyty Naukowe. Transport / Politechnika Śląska; 2021, 112; Bibliogr. 13 poz.
0209-3324
2450-1549
Pojawia się w:
Zeszyty Naukowe. Transport / Politechnika Śląska
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Selected technical issues of deep neural networks for image classification purposes
Autorzy:
Grochowski, Michał
Kwasigroch, A.
Mikołajczyk, A.
Powiązania:
https://bibliotekanauki.pl/articles/200871.pdf
Data publikacji:
2019
Wydawca:
Polska Akademia Nauk. Czytelnia Czasopism PAN
Tematy:
deep neural network
deep learning
image classification
batch normalization
transfer learning
dropout
sieć neuronowa
klasyfikacja obrazów
normalizacja
transfer nauki
uczenie głębokie
Opis:
In recent years, deep learning and especially deep neural networks (DNN) have obtained amazing performance on a variety of problems, in particular in classification or pattern recognition. Among many kinds of DNNs, the convolutional neural networks (CNN) are most commonly used. However, due to their complexity, there are many problems related but not limited to optimizing network parameters, avoiding overfitting and ensuring good generalization abilities. Therefore, a number of methods have been proposed by the researchers to deal with these problems. In this paper, we present the results of applying different, recently developed methods to improve deep neural network training and operating. We decided to focus on the most popular CNN structures, namely on VGG based neural networks: VGG16, VGG11 and proposed by us VGG8. The tests were conducted on a real and very important problem of skin cancer detection. A publicly available dataset of skin lesions was used as a benchmark. We analyzed the influence of applying: dropout, batch normalization, model ensembling, and transfer learning. Moreover, the influence of the type of activation function was checked. In order to increase the objectivity of the results, each of the tested models was trained 6 times and their results were averaged. In addition, in order to mitigate the impact of the selection of learning, test and validation sets, k-fold validation was applied.
Źródło:
Bulletin of the Polish Academy of Sciences. Technical Sciences; 2019, 67, 2; 363-376
0239-7528
Pojawia się w:
Bulletin of the Polish Academy of Sciences. Technical Sciences
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
A genetic algorithm based optimized convolutional neural network for face recognition
Autorzy:
Karlupia, Namrata
Mahajan, Palak
Abrol, Pawanesh
Lehana, Parveen K.
Powiązania:
https://bibliotekanauki.pl/articles/2201023.pdf
Data publikacji:
2023
Wydawca:
Uniwersytet Zielonogórski. Oficyna Wydawnicza
Tematy:
convolutional neural network
genetic algorithm
deep learning
evolutionary technique
sieć neuronowa konwolucyjna
algorytm genetyczny
uczenie głębokie
technika ewolucyjna
Opis:
Face recognition (FR) is one of the most active research areas in the field of computer vision. Convolutional neural networks (CNNs) have been extensively used in this field due to their good efficiency. Thus, it is important to find the best CNN parameters for its best performance. Hyperparameter optimization is one of the various techniques for increasing the performance of CNN models. Since manual tuning of hyperparameters is a tedious and time-consuming task, population based metaheuristic techniques can be used for the automatic hyperparameter optimization of CNNs. Automatic tuning of parameters reduces manual efforts and improves the efficiency of the CNN model. In the proposed work, genetic algorithm (GA) based hyperparameter optimization of CNNs is applied for face recognition. GAs are used for the optimization of various hyperparameters like filter size as well as the number of filters and of hidden layers. For analysis, a benchmark dataset for FR with ninety subjects is used. The experimental results indicate that the proposed GA-CNN model generates an improved model accuracy in comparison with existing CNN models. In each iteration, the GA minimizes the objective function by selecting the best combination set of CNN hyperparameters. An improved accuracy of 94.5% is obtained for FR.
Źródło:
International Journal of Applied Mathematics and Computer Science; 2023, 33, 1; 21--31
1641-876X
2083-8492
Pojawia się w:
International Journal of Applied Mathematics and Computer Science
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Deep adversarial neural network for specific emitter identification under varying frequency
Autorzy:
Huang, Keju
Yang, Junan
Liu, Hui
Hu, Pengjiang
Powiązania:
https://bibliotekanauki.pl/articles/2173603.pdf
Data publikacji:
2021
Wydawca:
Polska Akademia Nauk. Czytelnia Czasopism PAN
Tematy:
specific emitter identification
unsupervised domain adaptation
transfer learning
deep learning
identyfikacja emitera konkretna
adaptacja domeny nienadzorowana
transfer uczenia się
uczenie głębokie
Opis:
Specific emitter identification (SEI) is the process of identifying individual emitters by analyzing the radio frequency emissions, based on the fact that each device contains unique hardware imperfections. While the majority of previous research focuses on obtaining features that are discriminative, the reliability of the features is rarely considered. For example, since device characteristics of the same emitter vary when it is operating at different carrier frequencies, the performance of SEI approaches may degrade when the training data and the test data are collected from the same emitters with different frequencies. To improve performance of SEI under varying frequency, we propose an approach based on continuous wavelet transform (CWT) and domain adversarial neural network (DANN). The proposed approach exploits unlabeled test data in addition to labeled training data, in order to learn representations that are discriminative for individual emitters and invariant for varying frequencies. Experiments are conducted on received signals of five emitters under three carrier frequencies. The results demonstrate the superior performance of the proposed approach when the carrier frequencies of the training data and the test data differ.
Źródło:
Bulletin of the Polish Academy of Sciences. Technical Sciences; 2021, 69, 2; art. no. e136737
0239-7528
Pojawia się w:
Bulletin of the Polish Academy of Sciences. Technical Sciences
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Deep adversarial neural network for specific emitter identification under varying frequency
Autorzy:
Huang, Keju
Yang, Junan
Liu, Hui
Hu, Pengjiang
Powiązania:
https://bibliotekanauki.pl/articles/2128144.pdf
Data publikacji:
2021
Wydawca:
Polska Akademia Nauk. Czytelnia Czasopism PAN
Tematy:
specific emitter identification
unsupervised domain adaptation
transfer learning
deep learning
identyfikacja emitera konkretna
adaptacja domeny nienadzorowana
transfer uczenia się
uczenie głębokie
Opis:
Specific emitter identification (SEI) is the process of identifying individual emitters by analyzing the radio frequency emissions, based on the fact that each device contains unique hardware imperfections. While the majority of previous research focuses on obtaining features that are discriminative, the reliability of the features is rarely considered. For example, since device characteristics of the same emitter vary when it is operating at different carrier frequencies, the performance of SEI approaches may degrade when the training data and the test data are collected from the same emitters with different frequencies. To improve performance of SEI under varying frequency, we propose an approach based on continuous wavelet transform (CWT) and domain adversarial neural network (DANN). The proposed approach exploits unlabeled test data in addition to labeled training data, in order to learn representations that are discriminative for individual emitters and invariant for varying frequencies. Experiments are conducted on received signals of five emitters under three carrier frequencies. The results demonstrate the superior performance of the proposed approach when the carrier frequencies of the training data and the test data differ.
Źródło:
Bulletin of the Polish Academy of Sciences. Technical Sciences; 2021, 69, 2; e136737, 1--9
0239-7528
Pojawia się w:
Bulletin of the Polish Academy of Sciences. Technical Sciences
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Combining Spectral Analysis with Artificial Intelligence in Heart Sound Study
Autorzy:
Kucharski, Dariusz
Kajor, Marcin
Grochala, Dominik
Iwaniec, Marek
Iwaniec, Joanna
Powiązania:
https://bibliotekanauki.pl/articles/102508.pdf
Data publikacji:
2019
Wydawca:
Stowarzyszenie Inżynierów i Techników Mechaników Polskich
Tematy:
deep learning
heart sound classification
convolutional neural network
machine learning
signal processing
uczenie głębokie
klasyfikacja dźwięku serca
splotowa sieć neuronowa
uczenie maszynowe
przetwarzanie sygnałów
Opis:
The auscultation technique has been widely used in medicine as a screening examination for ages. Nowadays, advanced electronics and effective computational methods aim to support the healthcare sector by providing dedicated solutions which help physicians and support diagnostic process. In this paper, we propose a machine learning approach for the analysis of heart sounds. We used the spectral analysis of acoustic signal to calculate feature vectors and tested a set of machine learning approaches to provide the most effective detection of cardiac disorders. Finally, we achieved 91% of sensitivity and 99% of positive predictivity for a designed algorithm based on convolutional neural network.
Źródło:
Advances in Science and Technology. Research Journal; 2019, 13, 2; 112-118
2299-8624
Pojawia się w:
Advances in Science and Technology. Research Journal
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Anonymous traffic classification based on three-dimensional Markov image and deep learning
Autorzy:
Tang, Xin
Li, Huanzhou
Zhang, Jian
Tang, Zhangguo
Wang, Han
Cai, Cheng
Powiązania:
https://bibliotekanauki.pl/articles/27311448.pdf
Data publikacji:
2023
Wydawca:
Polska Akademia Nauk. Czasopisma i Monografie PAN
Tematy:
anonymous network
traffic classification
three-dimensional Markov image
output self-attention
deep learning
sieć anonimowa
klasyfikacja ruchu
trójwymiarowy obraz Markowa
samouwaga wyjściowa
uczenie głębokie
Opis:
Illegal elements use the characteristics of an anonymous network hidden service mechanism to build a dark network and conduct various illegal activities, which brings a serious challenge to network security. The existing anonymous traffic classification methods suffer from cumbersome feature selection and difficult feature information extraction, resulting in low accuracy of classification. To solve this problem, a classification method based on three-dimensional Markov images and output self-attention convolutional neural network is proposed. This method first divides and cleans anonymous traffic data packets according to sessions, then converts the cleaned traffic data into three-dimensional Markov images according to the transition probability matrix of bytes, and finally inputs the images to the output self-attention convolution neural network to train the model and perform classification. The experimental results show that the classification accuracy and F1-score of the proposed method for Tor, I2P, Freenet, and ZeroNet can exceed 98.5%, and the average classification accuracy and F1-score for 8 kinds of user behaviors of each type of anonymous traffic can reach 93.7%. The proposed method significantly improves the classification effect of anonymous traffic compared with the existing methods.
Źródło:
Bulletin of the Polish Academy of Sciences. Technical Sciences; 2023, 71, 4; art. no. e145676
0239-7528
Pojawia się w:
Bulletin of the Polish Academy of Sciences. Technical Sciences
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
A hybrid approach of a deep learning technique for real-time ECG beat detection
Autorzy:
Patro, Kiran Kumar
Prakash, Allam Jaya
Samantray, Saunak
Pławiak, Joanna
Tadeusiewicz, Ryszard
Pławiak, Paweł
Powiązania:
https://bibliotekanauki.pl/articles/2172118.pdf
Data publikacji:
2022
Wydawca:
Uniwersytet Zielonogórski. Oficyna Wydawnicza
Tematy:
cardiac abnormalities
CAD
convolutional neural network
CNN
deep learning
ECG
electrocardiogram
supra ventricular ectopic beats
SVE
nieprawidłowości kardiologiczne
sieć neuronowa konwolucyjna
uczenie głębokie
EKG
elektrokardiogram
Opis:
This paper presents a new customized hybrid approach for early detection of cardiac abnormalities using an electrocardiogram (ECG). The ECG is a bio-electrical signal that helps monitor the heart’s electrical activity. It can provide health information about the normal and abnormal physiology of the heart. Early diagnosis of cardiac abnormalities is critical for cardiac patients to avoid stroke or sudden cardiac death. The main aim of this paper is to detect crucial beats that can damage the functioning of the heart. Initially, a modified Pan–Tompkins algorithm identifies the characteristic points, followed by heartbeat segmentation. Subsequently, a different hybrid deep convolutional neural network (CNN) is proposed to experiment on standard and real-time long-term ECG databases. This work successfully classifies several cardiac beat abnormalities such as supra-ventricular ectopic beats (SVE), ventricular beats (VE), intra-ventricular conduction disturbances beats (IVCD), and normal beats (N). The obtained classification results show a better accuracy of 99.28% with an F1 score of 99.24% with the MIT–BIH database and a descent accuracy of 99.12% with the real-time acquired database.
Źródło:
International Journal of Applied Mathematics and Computer Science; 2022, 32, 3; 455--465
1641-876X
2083-8492
Pojawia się w:
International Journal of Applied Mathematics and Computer Science
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Fast multispectral deep fusion networks
Autorzy:
Osin, V.
Cichocki, A.
Burnaev, E.
Powiązania:
https://bibliotekanauki.pl/articles/200648.pdf
Data publikacji:
2018
Wydawca:
Polska Akademia Nauk. Czytelnia Czasopism PAN
Tematy:
multispectral imaging
data fusion
deep learning
convolutional network
object detection
image segmentation
obrazowanie wielospektralne
fuzja danych
uczenie głębokie
sieci splotowe
wykrywanie obiektów
segmentacja obrazu
Opis:
Most current state-of-the-art computer vision algorithms use images captured by cameras, which operate in the visible spectral range as input data. Thus, image recognition systems that build on top of those algorithms can not provide acceptable recognition quality in poor lighting conditions, e.g. during nighttime. Another significant limitation of such systems is high demand for computational resources, which makes them impossible to use on low-powered embedded systems without GPU support. This work attempts to create an algorithm for pattern recognition that will consolidate data from visible and infrared spectral ranges and allow near real-time performance on embedded systems with infrared and visible sensors. First, we analyze existing methods of combining data from different spectral ranges for object detection task. Based on the analysis, an architecture of a deep convolutional neural network is proposed for the fusion of multi-spectral data. This architecture is based on the single shot multi-box detection algorithm. Comparison analysis of the proposed architecture with previously proposed solutions for the multi-spectral object detection task shows comparable or better detection accuracy with previous algorithms and significant improvement of the running time on embedded systems. This study was conducted in collaboration with Philips Lighting Research Lab and solutions based on the proposed architecture will be used in image recognition systems for the next generation of intelligent lighting systems. Thus, the main scientific outcomes of this work include an algorithm for multi-spectral pattern recognition based on convolutional neural networks, as well as a modification of detection algorithms for working on embedded systems.
Źródło:
Bulletin of the Polish Academy of Sciences. Technical Sciences; 2018, 66, 6; 875-889
0239-7528
Pojawia się w:
Bulletin of the Polish Academy of Sciences. Technical Sciences
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Analiza sceny przy użyciu głębokich sieci neuronowych typu YOLO
Scene analysis using YOLO neural network
Autorzy:
Mikołajczyk, Mateusz
Kwasigroch, Arkadiusz
Grochowski, Michał
Powiązania:
https://bibliotekanauki.pl/articles/267008.pdf
Data publikacji:
2019
Wydawca:
Politechnika Gdańska. Wydział Elektrotechniki i Automatyki
Tematy:
sztuczne sieci neuronowe
detekcja obiektów
przetwarzanie obrazu
uczenie głębokie
artificial neural networks
object detection
image processing
deep learning
Opis:
W artykule opisany został problem analizy sceny na obrazach oraz sekwencjach video. Zadanie analizy sceny polega na detekcji, lokalizacji i klasyfikacji obiektów znajdujących się na obrazach. Zaimplementowany system wykorzystuje głęboką sieć neuronową, której struktura oparta została na architekturze YOLO (You Only Look Once). Niskie zapotrzebowania obliczeniowe wybranej architektury pozwala na wykonywanie detekcji w czasie rzeczywistym z zadowalającą dokładnością. W pracy przeprowadzono również badania nad doborem odpowiedniego optymalizatora wykorzystywanego w procesie uczenia. Jako przykładową aplikację wybrano analizę ruchu ulicznego w której skład wchodzi wykrywanie i lokalizacja obiektów takich jak m.in. samochody, motocykle czy sygnalizacja świetlna. Systemy tego typu mogą być wykorzystywane w wszelkiego typu systemach analizy wizyjnej otoczenia np. w pojazdach autonomicznych, systemach automatycznej analizy video z kamer przemysłowych, systemach dozoru czy analizy zdjęć satelitarnych.
The paper describes the problem of scene analysis in images and video sequences. The task of scene analysis is to detect, locate and classify objects in images. As an example of an application, traffic analysis was chosen, which includes the detection and location of objects such as cars, motorcycles or traffic lights. The implemented system uses a deep neural network, whose structure is based on the YOLO (You Only Look Once) architecture. Low computing requirements of the chosen architecture allows performing real-time detection with satisfactory accuracy. The work also included a study on the selection of an appropriate optimizer used in the learning process. The program correctly detects objects with a large surface area, allowing the system to be used in traffic analysis. The work also showed that using the ADAM algorithm allowed significantly shorten the training time of the neural network. Systems of this type can be used in many types of video analysis systems such as autonomous vehicles, automatic video analysis systems with CCTV cameras, surveillance systems or satellite image analysis.
Źródło:
Zeszyty Naukowe Wydziału Elektrotechniki i Automatyki Politechniki Gdańskiej; 2019, 68; 37-40
1425-5766
2353-1290
Pojawia się w:
Zeszyty Naukowe Wydziału Elektrotechniki i Automatyki Politechniki Gdańskiej
Dostawca treści:
Biblioteka Nauki
Artykuł
Wyświetlanie 1-15 z 20

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