Temat: YOLO - Prolib Integro

Skocz do pozycji: 1.

Tytuł:: Detrimental Starfish Detection on Embedded System: A Case Study of YOLOv5 Deep Learning Algorithm and TensorFlow Lite framework
Autorzy:: Toan, Nguyen Quoc
Tematy:: deep learning
computer vision
YOLO
embedded system; Pokaż więcej
Wydawca:: Politechnika Lubelska. Instytut Informatyki
Powiązania:: https://bibliotekanauki.pl/articles/2086221.pdf Link otwiera się w nowym oknie
Opis:: There is a great range of spectacular coral reefs in the ocean world. Unfortunately, they are in jeopardy, due to an overabundance of one specific starfish called the coral-eating crown-of-thorns starfish (or COTS). This article provides research to deliver innovation in COTS control. Using a deep learning model based on the You Only Look Once version 5 (YOLOv5) deep learning algorithm on an embedded device for COTS detection. It aids professionals in optimizing their time, resources, and enhances efficiency for the preservation of coral reefs worldwide. As a result, the performance over the algorithm was outstanding with Precision: 0.93 - Recall: 0.77 - F1score: 0.84.
Dostawca treści:: Biblioteka Nauki

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Skocz do pozycji: 2.

Tytuł:: Implementing visual assistant using YOLO and SSD for visually-impaired persons
Autorzy:: Litoriya, Ratnesh
Bandhu, Kailash Chandra
Gupta, Sanket
Rajawat, IIshika
Jagwani, Hany
Yadav, Chirayu
Tematy:: YOLO
SSD
object detection
R-CNN
COCO; Pokaż więcej
Wydawca:: Sieć Badawcza Łukasiewicz - Przemysłowy Instytut Automatyki i Pomiarów
Powiązania:: https://bibliotekanauki.pl/articles/59115480.pdf Link otwiera się w nowym oknie
Opis:: Artificial Intelligence has been touted as the next big thing that is capable of altering the current landscape of the technological domain. Through the use of Artificial Intelligence and Machine Learning, pioneering work has been undertaken in the area of Visual and Object Detection. In this paper, we undertake the analysis of a Visual Assistant Application for Guiding Visually-Impaired Individuals. With recent breakthroughs in computer vision and supervised learning models, the problem at hand has been reduced significantly to the point where new models are easier to build and implement than the already existing models. Different object detection models exist now that provide object tracking and detection with great accuracy. These techniques have been widely used in automating detection tasks in different areas. A few newly discovered detection approaches, such as the YOLO (You Only Look Once) and SSD (Single Shot Detector) approaches, have proved to be consistent and quite accurate at detecting objects in real-time. This paper attempts to utilize the combination of these state-of-the-art, real-time object detection techniques to develop a good base model. This paper also implements a ’Visual Assistant’ for visually impaired people. The results obtained are improved and superior compared to existing algorithms.
Dostawca treści:: Biblioteka Nauki

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Skocz do pozycji: 3.

Tytuł:: Identification of Garbage in the River Based on The YOLO Algorithm
Autorzy:: Suprapto, Bhakti Yudho
Kelvin
Kurniawan, Muhammad Arief
Ardela, Muhammad Kevin
Hikmarika, Hera
Husin, Zainal
Dwijayanti, Suci
Tematy:: control
identification
HSV and sift method
USV
yolo algorithm; Pokaż więcej
Wydawca:: Polska Akademia Nauk. Czytelnia Czasopism PAN
Powiązania:: https://bibliotekanauki.pl/articles/2055273.pdf Link otwiera się w nowym oknie
Opis:: This paper discusses the identification of garbage using the YOLO algorithm. In the rivers, it is usually difficult to distinguish between garbage and plants, especially when it is done in real-time and at the time of too much light. Therefore, there is a need of an appropriate method. The HSV and SIFT methods were used as preliminary tests. The tests were quite successful even in close condition, however, there were still many problems faced in using this method since it is only based on pixel and shape readings. Meanwhile, YOLO algorithm was able to identify garbage and water hyacinth even though they were closed to each other.
Dostawca treści:: Biblioteka Nauki

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Skocz do pozycji: 4.

Tytuł:: Microscopic Studies of Activated Sludge Supported by Automatic Image Analysis Based on Deep Learning Neural Networks
Autorzy:: Dziadosz, Marcin
Majerek, Dariusz
Łagód, Grzegorz
Tematy:: activated sludge
automatic image analysis
deep learning
YOLO
Arcella vulgaris
biomarkers
bioindication; Pokaż więcej
Wydawca:: Polskie Towarzystwo Inżynierii Ekologicznej
Powiązania:: https://bibliotekanauki.pl/articles/59114470.pdf Link otwiera się w nowym oknie
Opis:: Paper presents microscopic studies of activated sludge supported by automatic image analysis based on deep learning neural networks. The organisms classified as Arcella vulgaris were chosen for the research. They frequently occur in the waters containing organic substances as well as WWTPs employing the activated sludge method. Usually, they can be clearly seen and counted using a standard optical microscope, as a result of their distinctive appearance, numerous population and passive behavior. Thus, these organisms constitute a viable object for detection task. Paper refers to the comparison of performance of deep learning networks namely YOLOv4 and YOLOv8, which conduct automatic image analysis of the afore-mentioned organisms. YOLO (You Only Look Once) constitutes a one-stage object detection model that look at the analyzed image once and allow real-time detection without a marked accuracy loss. The training of the applied YOLO models was carried out using sample microscopic images of activated sludge. The relevant training data set was created by manually labeling the digital images of organisms, followed by calculation and comparison of various metrics, including recall, precision, and accuracy. The architecture of the networks built for the detection task was general, which means that the structure of the layers and filters was not affected by the purpose of using the models. Accounting mentioned universal construction of the models, the results of the accuracy and quality of the classification can be considered as very good. This means that the general architecture of the YOLO networks can also be used for specific tasks such as identification of shell amoebas in activated sludge.
Dostawca treści:: Biblioteka Nauki

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Skocz do pozycji: 5.

Tytuł:: Integrated and deep learning–based social surveillance system : a novel approach
Autorzy:: Litoriya, Ratnesh
Ramchandani, Dev
Moyal, Dhruvansh
Bothra, Dhruv
Tematy:: Video Surveillance
object detection
object tracking
YOLO v4 algorithm
OpenCV; Pokaż więcej
Wydawca:: Sieć Badawcza Łukasiewicz - Przemysłowy Instytut Automatyki i Pomiarów
Powiązania:: https://bibliotekanauki.pl/articles/27314204.pdf Link otwiera się w nowym oknie
Opis:: In industry and research, big data applications are gaining a lot of traction and space. Surveillance videos contribute significantly to big unlabelled data. The aim of visual surveillance is to understand and determine object behavior. It includes static and moving object detection, as well as video tracking to comprehend scene events. Object detection algorithms may be used to identify items in any video scene. Any video surveillance system faces a significant challenge in detecting moving objects and differentiating between objects with same shapes or features. The primary goal of this work is to provide an integrated framework for quick overview of video analysis utilizing deep learning algorithms to detect suspicious activity. In greater applications, the detection method is utilized to determine the region where items are available and the form of objects in each frame. This video analysis also aids in the attainment of security. Security may be characterized in a variety of ways, such as identifying theft or violation of covid protocols. The obtained results are encouraging and superior to existing solutions with 97% accuracy.
Dostawca treści:: Biblioteka Nauki

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Skocz do pozycji: 6.

Tytuł:: Vehicle tracking and speed estimation under mixed traffic conditions using YOLOV4 and sort: a case study of Hanoi
Autorzy:: Vuong, Xuan Can
Mou, Rui-Fang
Vu, Trong Thuat
Tematy:: vehicle tracking
speed estimation
mixed traffic conditions
YOLO
SORT
śledzenie pojazdu
szacowanie prędkości
warunki ruchu mieszanego; Pokaż więcej
Wydawca:: Politechnika Śląska. Wydawnictwo Politechniki Śląskiej
Powiązania:: https://bibliotekanauki.pl/articles/2203861.pdf Link otwiera się w nowym oknie
Opis:: This paper presents a method to estimate vehicle speed automatically, including cars and motorcycles under mixed traffic conditions from video sequences acquired with stationary cameras in Hanoi City of Vietnam. The motion of the vehicle is detected and tracked along the frames of the video sequences using YOLOv4 and SORT algorithms with a custom dataset. In the method, the distance traveled by the vehicle is the length of virtual point-detectors, and the travel time of the vehicle is calculated using the movement of the centroid over the entrance and exit of virtual point-detectors (i.e., region of interest), and then the speed is also estimated based on the traveled distance and the travel time. The results of two experimental studies showed that the proposed method had small values of MAPE (within 3%), proving that the proposed method is reliable and accurate for application in real-world mixed traffic environments like Hanoi, Vietnam.
Dostawca treści:: Biblioteka Nauki

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Skocz do pozycji: 7.

Tytuł:: Project and implementation of artificial intelligence for Match-3 game using machine learning technologies
Projekt i implementacja sztucznej inteligencji dla gry Match-3 z wykorzystaniem uczenia maszynowego
Autorzy:: Kapałka, Kamil
Opis:: The subject of the master's dissertation was an attempt to create an artificial intelligence capable of playing various Match-3 games on the basis of a custom-made game prototype.The YOLO algorithm was used to detect elements on the game board, and the AlphaZero algorithm was used to calculate the optimal moves to be made.The neural network for YOLO algorithm was trained via a large set of generated graphic files containing elements of the board of a given game on various backgrounds, and the neural network for the AlphaZero algorithm - via reinforcement learning, by playing the prototype game.The algorithm's effectiveness was afterward tested by comparing its results of the gameplay with the results achieved by a human player - both in the prototype game and in one of the chosen games of this genre, Candy Crush Saga - in a series of several playoffs on different boards.The test results showed that the knowledge acquired by the algorithm while playing the prototype game did not translate into the ability to play commercially available titles in a satisfactory manner. The reason for this state of affairs is, among others, the nuances of the game itself, where the interactions between the board elements uniquely implemented in the game are not known to the AlphaZero algorithm, which makes it unable to arrive at the correct movement on the board.
Tematem pracy magisterskiej była próba utworzenia sztucznej inteligencji zdolnej do prowadzenia rozgrywki w różne gry z gatunku Match-3 na podstawie utworzonego na potrzeby pracy prototypu.Wykorzystano algorytm YOLO w celu wykrywania elementów na planszy, oraz algorytm AlphaZero w celu obliczania optymalnych ruchów do wykonania.Sieć neuronowa YOLO była uczona na podstawie kompletu wygenerowanych plików graficznych zawierających elementy planszy danej gry na różnych tłach, a sieć neuronowa AlphaZero - na podstawie prowadzenia rozgrywki wewnątrz utworzonej prototypowej gry Match-3.Przeprowadzono test efektywności algorytmu poprzez porównanie wyników prowadzonej przez niego rozgrywki z wynikami osiąganymi przez ludzkiego gracza - zarówno w prototypową grę, jak i w jeden z wybranych tytułów gier z tego gatunku, Candy Crush Saga - w serii kilku rozgrywek, na różnych planszach.Wyniki testów wskazały na to, że wiedza nabyta przez algorytm podczas gry w prototypową grę nie przekładają się na zdolność grania w dostępne na rynku tytuły w zadowalający sposób. Przyczyną takiego stanu rzeczy są między innymi niuanse rozgrywki, gdzie unikatowo zaimplementowane w grze interakcje między elementami planszy nie są wiadome algorytmowi AlphaZero, przez co nie jest on zdolny wyprowadzić na planszy poprawnego ruchu.
Dostawca treści:: Repozytorium Uniwersytetu Jagiellońskiego

Inne

na półce

Skocz do pozycji: 8.

Tytuł:: Soft computing techniques-based digital video forensics for fraud medical anomaly detection
Autorzy:: Nanda, Sunpreet Kaur
Ghai, Deepika
Ingole, P.V.
Pande, Sagar
Tematy:: smart healthcare system
medical imaging
healthcare fraud
MRI imaging
digital image forensics
object detection
YOLO architecture
customized CNN
inteligentny system opieki zdrowotnej
obrazowanie medyczne
oszustwo w służbie zdrowia
obrazowanie MRI
kryminalistyka obrazu cyfrowego
detekcja obiektów
architektura YOLO
dostosowanie CNN; Pokaż więcej
Wydawca:: Instytut Podstawowych Problemów Techniki PAN
Powiązania:: https://bibliotekanauki.pl/articles/38701161.pdf Link otwiera się w nowym oknie
Opis:: The current pandemic situation has made it important for everyone to wear masks. Digital image forensics plays an important role in preventing medical fraud and in object detection. It is helpful in avoiding the high-risk situations related to the health and security of the individuals or the society, including getting the proper evidence for identifying the people who are not wearing masks. A smart system can be developed based on the proposed soft computing technique, which can be helpful to detect precisely and quickly whether a person wears a mask or not and whether he/she is carrying a gun. The proposed method gave 100% accurate results in videos used to test such situations. The system was able to precisely differentiate between those wearing a mask and those not wearing a mask. It also effectively detects guns, which can be used in many applications where security plays an important role, such as the military, banks, etc.
Dostawca treści:: Biblioteka Nauki

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