1. Detection of AI-generated synthetic images with a lightweight CNNAdrian Lokner Lađević, Tin Kramberger, Renata Kovačević, Dino Vlahek, 2024, izvirni znanstveni članek Ključne besede: convolutional neural networks, generative adversarial networks, classification, synthetic images, explanable artificial intelligence Objavljeno v DKUM: 29.01.2025; Ogledov: 0; Prenosov: 2
Celotno besedilo (36,84 MB) |
2. Rapid assessment of steel machinability through spark analysis and data-mining techniquesGoran Munđar, Miha Kovačič, Miran Brezočnik, Krzysztof Stępień, Uroš Župerl, 2024, izvirni znanstveni članek Opis: The machinability of steel is a crucial factor in manufacturing, influencing tool life, cutting
forces, surface finish, and production costs. Traditional machinability assessments are labor-intensive
and costly. This study presents a novel methodology to rapidly determine steel machinability using
spark testing and convolutional neural networks (CNNs). We evaluated 45 steel samples, including
various low-alloy and high-alloy steels, with most samples being calcium steels known for their
superior machinability. Grinding experiments were conducted using a CNC machine with a ceramic
grinding wheel under controlled conditions to ensure a constant cutting force. Spark images captured
during grinding were analyzed using CNN models with the ResNet18 architecture to predict V15
values, which were measured using the standard ISO 3685 test. Our results demonstrate that the
created prediction models achieved a mean absolute percentage error (MAPE) of 12.88%. While
some samples exhibited high MAPE values, the method overall provided accurate machinability
predictions. Compared to the standard ISO test, which takes several hours to complete, our method is
significantly faster, taking only a few minutes. This study highlights the potential for a cost-effective
and time-efficient alternative testing method, thereby supporting improved manufacturing processes. Ključne besede: steel machinability, spark testing, data mining, machine vision, convolutional neural networks Objavljeno v DKUM: 12.09.2024; Ogledov: 15; Prenosov: 14
Celotno besedilo (5,24 MB) Gradivo ima več datotek! Več... |
3. A waste separation system based on sensor technology and deep learning: a simple approach applied to a case study of plastic packaging wasteRok Pučnik, Monika Dokl, Yee Van Fan, Annamaria Vujanović, Zorka Novak-Pintarič, Kathleen B. Aviso, Raymond R. Tan, Bojan Pahor, Zdravko Kravanja, Lidija Čuček, 2024, izvirni znanstveni članek Ključne besede: waste management, smart waste bin system, central post-sorting, sensor technology, deep learning, convolutional neural networks Objavljeno v DKUM: 23.08.2024; Ogledov: 51; Prenosov: 8
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4. UAV Thermal Imaging for Unexploded Ordnance Detection by Using Deep LearningMilan Bajić, Jr., Božidar Potočnik, 2023, izvirni znanstveni članek Opis: A few promising solutions for thermal imaging Unexploded Ordnance (UXO) detection were proposed after the start of the military conflict in Ukraine in 2014. At the same time, most of the landmine clearance protocols and practices are based on old, 20th-century technologies. More than 60 countries worldwide are still affected by explosive remnants of war, and new areas are contaminated almost every day. To date, no automated solutions exist for surface UXO detection by using thermal imaging. One of the reasons is also that there are no publicly available data. This research bridges both gaps by introducing an automated UXO detection method, and by publishing thermal imaging data. During a project in Bosnia and Herzegovina in 2019, an organisation, Norwegian People's Aid, collected data about unexploded ordnances and made them available for this research. Thermal images with a size of 720 x 480 pixels were collected by using an Unmanned Aerial Vehicle at a height of 3 m, thus achieving a very small Ground Sampling Distance (GSD). One of the goals of our research was also to verify if the explosive war remnants' detection accuracy could be improved further by using Convolutional Neural Networks (CNN). We have experimented with various existing modern CNN architectures for object identification, whereat the YOLOv5 model was selected as the most promising for retraining. An eleven-class object detection problem was solved primarily in this study. Our data were annotated semi-manually. Five versions of the YOLOv5 model, fine-tuned with a grid-search, were trained end-to-end on randomly selected 640 training and 80 validation images from our dataset. The trained models were verified on the remaining 88 images from our dataset. Objects from each of the eleven classes were identified with more than 90% probability, whereat the Mean Average Precision (mAP) at a 0.5 threshold was 99.5%, and the mAP at thresholds from 0.5 to 0.95 was 87.0% up to 90.5%, depending on the model's complexity. Our results are comparable to the state-of-the-art, whereat these object detection methods have been tested on other similar small datasets with thermal images. Our study is one of the few in the field of Automated UXO detection by using thermal images, and the first that solves the problem of identifying more than one class of objects. On the other hand, publicly available thermal images with a relatively small GSD will enable and stimulate the development of new detection algorithms, where our method and results can serve as a baseline. Only really accurate automatic UXO detection solutions will help to solve one of the least explored worldwide life-threatening problems. Ključne besede: unmanned aerial vehicle, unexploded ordnance, thermal imaging, UXOTi_NPA dataset, convolutional neural networks, deep learning Objavljeno v DKUM: 12.02.2024; Ogledov: 389; Prenosov: 21
Celotno besedilo (16,94 MB) Gradivo ima več datotek! Več... |