Iason Katsamenis

National Technical University of Athens

Author Statistics

Papers

Citation

H-Index

i-10 index

Research Trends

Author Order

Document Type

Co-Authors

Eftychios Protopapadakis

National Technical University of Athens

Nikolaos Doulamis

National Technical University of Athens

Anastasios Doulamis

National Technical University of Athens

Athanasios Voulodimos

National Technical University of Athens

Nikolaos Bakalos

National Technical University of Athens

Eleni Eirini Karolou

National Technical University of Athens

Dimitris Kalogeras

National Technical University of Athens

Miquel Cantero

Robotnik (Spain)

Anastasios Doulamis

National Technical University of Athens

Franziska Schmidt

Université Gustave Eiffel

Cooperative Institutions

National Technical University of Athens

Institute of Communication and Computer Systems

National and Kapodistrian University of Athens

Université Gustave Eiffel

Academy of Athens

Technical University of Crete

Centre des Matériaux

Exodus (Greece)

Robotnik (Spain)

University of Cambridge

Author Statistics

Papers

Citation

H-Index

i-10 index

Research Field

Simultaneous Precise Localization And Classification of metal rust defects for robotic-driven maintenance and prefabrication using residual attention U-Net

Automation in Construction (2022)

Iason Katsamenis Nikolaos Doulamis Anastasios Doulamis Eftychios Protopapadakis Athanasios Voulodimos

Transputer

RGB color model

Prefabrication

10.1016/j.autcon.2022.104182

Cite

Citations (37)

Man Overboard: Fall detection using spatiotemporal convolutional autoencoders in maritime environments

Nikolaos Bakalos Iason Katsamenis Athanasios Voulodimos

Man overboard incidents in a maritime vessel are serious accidents where, the efficient and rapid detection is crucial in the recovery of the victim. The severity of such accidents, urge the use of intelligent systems that are able to automatically detect a fall and provide relevant alerts. To this end the use of novel deep learning and computer vision algorithms have been tested and proved efficient in problems with similar structure. This paper presents the use of a deep learning framework for automatic detection of man overboard incidents. We investigate the use of simple RGB video streams for extracting specific properties of the scene, such as movement and saliency, and use convolutional spatiotemporal autoencoders to model the normal conditions and identify anomalies. Moreover, in this work we present a dataset that was created to train and test the efficacy of our approach.

RGB color model

10.1145/3453892.3461326

Cite

Citations (3)

TraCon: A Novel Dataset for Real-Time Traffic Cones Detection Using Deep Learning

Lecture notes in networks and systems (2022)

Iason Katsamenis Eleni Eirini Karolou Agapi Davradou Eftychios Protopapadakis Anastasios Doulamis

RGB color model

10.1007/978-3-031-17601-2_37

Cite

Citations (26)

A Few-Shot Attention Recurrent Residual U-Net for Crack Segmentation

arXiv (Cornell University) (2023)

Iason Katsamenis Eftychios Protopapadakis Nikolaos Bakalos Anastasios Doulamis Nikolaos Doulamis

Recent studies indicate that deep learning plays a crucial role in the automated visual inspection of road infrastructures. However, current learning schemes are static, implying no dynamic adaptation to users' feedback. To address this drawback, we present a few-shot learning paradigm for the automated segmentation of road cracks, which is based on a U-Net architecture with recurrent residual and attention modules (R2AU-Net). The retraining strategy dynamically fine-tunes the weights of the U-Net as a few new rectified samples are being fed into the classifier. Extensive experiments show that the proposed few-shot R2AU-Net framework outperforms other state-of-the-art networks in terms of Dice and IoU metrics, on a new dataset, named CrackMap, which is made publicly available at https://github.com/ikatsamenis/CrackMap.

Domain Adaptation

Retraining

Net (polyhedron)

Residual neural network

Dice

Visual inspection

10.48550/arxiv.2303.01582

Cite

Citations (0)

Robotic Maintenance of Road Infrastructures: The HERON Project

Iason Katsamenis Matthaios Bimpas Eftychios Protopapadakis Charalampos Zafeiropoulos Dimitris Kalogeras

Of all public assets, road infrastructure tops the list. Roads are crucial for economic development and growth, providing access to education, health, and employment. The maintenance, repair, and upgrade of roads are therefore vital to road users' health and safety as well as to a well-functioning and prosperous modern economy. The EU-funded HERON project will develop an integrated automated system to adequately maintain road infrastructure. In turn, this will reduce accidents, lower maintenance costs, and increase road network capacity and efficiency. To coordinate maintenance works, the project will design an autonomous ground robotic vehicle that will be supported by autonomous drones. Sensors and scanners for 3D mapping will be used in addition to artificial intelligence toolkits to help coordinate road maintenance and upgrade workflows.

Upgrade

Drone

10.1145/3529190.3534746

Cite

Citations (16)

Caractérisation des chaussées urbaines démontables : traitement des données par IA

G. Andreoli Franziska Schmidt Thierry Sedran Amine Ihamouten Éric Gennesseaux

Source

Cite

Citations (0)

Deep learning models for COVID-19 infected area segmentation in CT images

Athanasios Voulodimos Eftychios Protopapadakis Iason Katsamenis Anastasios Doulamis Nikolaos Doulamis

Recent studies indicated that detecting radiographic patterns on CT chest scans can yield high sensitivity and specificity for COVID-19 detection. In this work, we scrutinize the effectiveness of deep learning models for semantic segmentation of pneumonia infected area segmentation in CT images for the detection of COVID-19. We explore the efficacy of U-Nets and Fully Convolutional Neural Networks in this task using real-world CT data from COVID-19 patients. The results indicate that Fully Convolutional Neural Networks are capable of accurate segmentation despite the class imbalance on the dataset and the man-made annotation errors on the boundaries of symptom manifestation areas, and can be a promising method for further analysis of COVID-19 induced pneumonia symptoms in CT images.

10.1145/3453892.3461322

Cite

Citations (42)

Multi-label deep learning models for continuous monitoring of road infrastructures

Eftychios Protopapadakis Iason Katsamenis Anastasios Doulamis

A multi-class, multi-label deep learning model for the monitoring of road infrastructures is presented in this paper. The employed detection methodology can identify animals, debris, road defects, fire, fog, flooded areas and humans. All these categories are strongly related to the efficient movement of vehicles through a transportation network. Possible detections indicate roadway disruptions of various types. Therefore, they should be detected as fast as possible. Experimental results indicate that the proposed scheme presents high detection results and, thus, can be used in any motorway monitoring process.

10.1145/3389189.3397997

Cite

Citations (16)

Man overboard event detection from RGB and thermal imagery

Iason Katsamenis Eftychios Protopapadakis Athanasios Voulodimos Dimitris Dres Dimitris Drakoulis

A man overboard is an emergency incident, in which fast detection is the most crucial factor, for the quickest and most efficient recovery of the victim. As such, efficient monitoring methodologies should be employed. A variety of sensors is available today, supporting a continuous monitoring process, regardless of environmental conditions; RGB and thermal are two commonly used sensors. At the same time, several algorithms and techniques have been tested and proved to be efficient in human detection and situation recognition tasks. However, to this day a coherent methodology for fall detection over multiple sensors on a large-scale deployment, complying with related ISO standards on extremely low false positive alerts, has not been implemented. In this paper, we investigate the possibilities as well as the limitations of man overboard vision-based systems' development based on RGB and thermal imagery.

RGB color model

10.1145/3389189.3397998

Cite

Citations (15)

Robotic Maintenance of Road Infrastructures: The HERON Project

arXiv (Cornell University) (2022)

Iason Katsamenis Matthaios Bimpas Eftychios Protopapadakis Charalampos Zafeiropoulos Dimitris Kalogeras

Upgrade

Drone

10.48550/arxiv.2205.04164

Cite

Citations (0)