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Padnet: a Patch-Based Anomaly Detection Framework for Industrial Pipeline Damage Detection
Erofili Alexaki;Christos Papaioannidis;Vasileios Mygdalis;Ioannis Pitas
ICASSP 2025 - 2025 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
Year: 2025 | Conference Paper |
Industrial pipeline inspection in petrochemical refineries is dangerous, expensive, time-consuming and prone to errors. Anomaly detection can play a crucial role towards its automation. Damages in this type of infrastructure are few and can be considered as anomalies (essentially outliers). This paper proposes a novel patch-based Anomaly Detection Network (PADNet), that employs deep learning for d...Show More
SNE-RoadSegV2: Advancing Heterogeneous Feature Fusion and Fallibility Awareness for Freespace Detection
Yi Feng;Yu Ma;Stepan Andreev;Qijun Chen;Alexander Dvorkovich;Ioannis Pitas;Rui Fan
Feature-fusion networks with duplex encoders have proved to be an effective technique for solving the road freespace detection problem. However, despite the compelling results achieved by previous research efforts, the exploration of adequate and discriminative heterogeneous feature fusion, as well as the development of fallibility-aware loss functions, remains relatively scarce. This article make...Show More
These Maps Are Made by Propagation: Adapting Deep Stereo Networks to Road Scenarios With Decisive Disparity Diffusion
Chuang-Wei Liu;Yikang Zhang;Qijun Chen;Ioannis Pitas;Rui Fan
Stereo matching has emerged as a cost-effective solution for road surface 3D reconstruction, garnering significant attention towards improving both computational efficiency and accuracy. This article introduces decisive disparity diffusion (D3Stereo), marking the first exploration of dense deep feature matching that adapts pre-trained deep convolutional neural networks (DCNNs) to previously unseen...Show More
Forest Fire Segmentation Metrics and Algorithms
2024 International Conference on Information and Communication Technologies for Disaster Management (ICT-DM)
Year: 2024 | Conference Paper |
In this paper, we present a comprehensive comparative analysis of algorithms for forest fire image segmentation. Specifically, the CNN-based BiSeNet, I2I-CNN, and PID-Net region segmentation algorithms were applied on Unmanned Aerial Vehicle (UAV) fire images for segmenting fire regions. To this end, we propose the implementation of novel region segmentation metrics tailored specifically for fores...Show More
Flooded house detection for natural disaster managment
2024 International Conference on Information and Communication Technologies for Disaster Management (ICT-DM)
Year: 2024 | Conference Paper |
In this paper we propose a method for submerged houses roof detection on a region with a flood natural disaster by a drone on the fly. We try two different object detection architecture, YOLOv6 and DETR, to find out which one has better performance for a drone application. We propose three different image datasets, namely Floodnet dataset, Giannitsa dataset and RedRoofs dataset merged into one lar...Show More
Efficient Data Utilization in Deep Neural Networks for Inference Reliability
2024 IEEE International Conference on Image Processing Challenges and Workshops (ICIPCW)
Year: 2024 | Conference Paper |
In today’s data-driven world, the exponential growth of data across various sectors presents unique opportunities and challenges. In this paper, we propose a novel method tailored to enhance the efficiency of Deep Neural Networks (DNNs) in managing these vast data amounts. The primary challenge addressed is the ability of DNNs to provide inferences on the minimal amount of data without sacrificing...Show More
Forest Fire Image Classification Through Decentralized DNN Inference
2024 IEEE International Conference on Image Processing Challenges and Workshops (ICIPCW)
Year: 2024 | Conference Paper |
In the realm of Natural Disaster Management (NDM), timely communication with local authorities is paramount for an effective response. To achieve this, multi-agent systems play a pivotal role by proficiently identifying and categorizing various disasters. In the field of Distributed Deep Neural Network (D-DNN) inference, such approaches often require DNN nodes to transmit their results to the clou...Show More
Domain Expertise Assessment for Multi-DNN Agent Systems
2024 IEEE Symposium on Computers and Communications (ISCC)
Year: 2024 | Conference Paper |
Recently, multi-agent systems that facilitate knowledge sharing among Deep Neural Network (DNN) agents, have gained increasing attention. This paper explores the dynamics of multi-agent systems that support Teacher-Student DNN interactions, where knowledge is distilled from Teachers to Students. Within such systems, selecting the most compatible Teacher for a given task is far from trivial and can...Show More
Leveraging Collective Knowledge for Forest Fire Classification
2024 IEEE Symposium on Computers and Communications (ISCC)
Year: 2024 | Conference Paper |
This paper presents a novel Fire Classification Multi-Agent (FCMA) framework that utilizes peer-to-peer learning and distributed learning techniques to disseminate knowledge within the agent community. Furthermore, we define and introduce the architecture of a Deep Neural Network (DNN) agent, which can infinitely interact with other DNN agents and the external environment upon deployment. The FCMA...Show More
Proof of Quality Inference (PoQI): An AI Consensus Protocol for Decentralized DNN Inference Frameworks
2024 IEEE Symposium on Computers and Communications (ISCC)
Year: 2024 | Conference Paper |
In the realm of machine learning systems, achieving consensus among networking nodes is a fundamental yet challenging task. This paper presents Proof of Quality Inference (PoQI), a novel consensus protocol designed to integrate deep learning inference under the basic format of the Practical Byzantine Fault Tolerant (P-BFT) algorithm. PoQI is applied to Deep Neural Networks (DNNs) to infer the qual...Show More
Lightweight Human Gesture Recognition Using Multimodal Features
Anestis Christidis;Christos Papaioannidis;Ioannis Mademlis;Ioannis Pitas
2024 32nd European Signal Processing Conference (EUSIPCO)
Year: 2024 | Conference Paper |
Human gesture recognition is a very important tool in human-computer or human-robot interaction. In many cases, such algorithms may need to be executed on systems with limited computational capabilities, due to size or weight constraints, introducing restrictions that can impede gesture recognition performance. This paper proposes a gesture recognition method that is based on a very simple and lig...Show More
Generative Representation Learning in Recurrent Neural Networks for Causal Timeseries Forecasting
Feed-forward deep neural networks (DNNs) are the state of the art in timeseries forecasting. A particularly significant scenario is the causal one: when an arbitrary subset of variables of a given multivariate timeseries is specified as forecasting target, with the remaining ones (exogenous variables) causing the target at each time instance. Then, the goal is to predict a temporal window of futur...Show More
Real-Time Object Geopositioning from Monocular Target Detection/Tracking for Aerial Cinematography
Daniel Aláez;Vasileios Mygdalis;Jesús Villadangos;Ioannis Pitas
2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP)
Year: 2023 | Conference Paper |
In recent years, the field of automated aerial cinematography has seen a significant increase in demand for real-time 3D target geopositioning for motion and shot planning. To this end, many of the existing cinematography plans require the use of complex sensors that need to be equipped on the subject or rely on external motion systems. This work addresses this problem by combining monocular visua...Show More
GreekPolitics: Sentiment Analysis on Greek Politically Charged Tweets
Emmanouil Patsiouras;Ioanna Koroni;Ioannis Mademlis;Ioannis Pitas
2023 31st European Signal Processing Conference (EUSIPCO)
Year: 2023 | Conference Paper |
Cited by: Papers (1)
The rapid growth of on-line social media platforms has rendered opinion mining/sentiment analysis a critical area of Natural Language Processing (NLP) research. This paper focuses on analyzing Twitter posts (tweets), written in the Greek language and politically charged in content. This is a rather underexplored topic, due to the scarcity of publicly available annotated datasets. Thus, we present ...Show More
AdvRevGAN: On Reversible Universal Adversarial Attacks for Privacy Protection Applications
2023 IEEE 33rd International Workshop on Machine Learning for Signal Processing (MLSP)
Year: 2023 | Conference Paper |
Different adversarial attack methods have been proposed in the literature, mainly focusing on attack efficiency and visual quality, e.g., similarity with the non-adversarial examples. These properties enable the use of adversarial attacks for privacy protection against automated classification systems, while maintaining utility for human users. In this paradigm, when privacy restrictions are lifte...Show More
Domain Adaptation in Power Line Segmentation: A New Synthetic Dataset
2023 IEEE International Conference on Image Processing (ICIP)
Year: 2023 | Conference Paper |
Power line segmentation is a critical component of UAV intelligent inspection systems to ensure the safe and reliable operation of power grids. For challenging-to-label tasks like this, simulators can efficiently generate large amounts of labeled data. In this work, a large-scale annotated synthetic power lines dataset generated utilizing the unity game engine and the unity perception package 1. T...Show More
Deep Reinforcement Learning with semi-expert distillation for autonomous UAV cinematography
Andreas Sochopoulos;Ioannis Mademlis;Evangelos Charalampakis;Sotirios Papadopoulos;Ioannis Pitas
2023 IEEE International Conference on Multimedia and Expo (ICME)
Year: 2023 | Conference Paper |
Cited by: Papers (1)
Unmanned Aerial Vehicles (UAVs, or drones) have revolutionized modern media production. Being rapidly deployable "flying cameras", they can easily capture aesthetically pleasing aerial footage of static or moving filming targets/subjects. Current approaches rely either on manual UAV/gimbal control by human experts, or on a combination of complex computer vision algorithms and hardware configuratio...Show More
Fast Single-Person 2D Human Pose Estimation Using Multi-Task Convolutional Neural Networks
ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
Year: 2023 | Conference Paper |
Cited by: Papers (9)
This paper presents a novel neural module for enhancing existing fast and lightweight 2D human pose estimation CNNs, in order to increase their accuracy. A baseline stem CNN is augmented by a collateral module, which is tasked to encode global spatial and semantic information and provide it to the stem network during inference. The latter one outputs the final 2D human pose estimations. Since glob...Show More
Exploiting One-Class Classification Optimization Objectives for Increasing Adversarial Robustness
ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
Year: 2023 | Conference Paper |
This work examines the problem of increasing the robustness of deep neural network-based image classification systems to adversarial attacks, without changing the neural architecture or employ adversarial examples in the learning process. We attribute their famous lack of robustness to the geometric properties of the deep neural network embedding space, derived from standard optimization options, ...Show More
Efficient Feature Extraction for Non-Maximum Suppression in Visual Person Detection
Charalampos Symeonidis;Ioannis Mademlis;Ioannis Pitas;Nikos Nikolaidis
ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
Year: 2023 | Conference Paper |
Cited by: Papers (2)
Non-Maximum Suppression (NMS) is a post-processing step in almost every visual object detector, tasked with rapidly pruning the number of overlapping detected candidate rectangular Regions-of-Interest (RoIs) and replacing them with a single, more spatially accurate detection (in pixel coordinates). The common Greedy NMS algorithm suffers from drawbacks, due to the need for careful manual tuning. I...Show More
Neural Attention-Driven Non-Maximum Suppression for Person Detection
Charalampos Symeonidis;Ioannis Mademlis;Ioannis Pitas;Nikos Nikolaidis
Non-maximum suppression (NMS) is a post-processing step in almost every visual object detector. NMS aims to prune the number of overlapping detected candidate regions-of-interest (RoIs) on an image, in order to assign a single and spatially accurate detection to each object. The default NMS algorithm (GreedyNMS) is fairly simple and suffers from severe drawbacks, due to its need for manual tuning....Show More
Fast CNN-Based Single-Person 2D Human Pose Estimation for Autonomous Systems
This paper presents a novel Convolutional Neural Network (CNN) architecture for 2D human pose estimation from RGB images that balances between high 2D human pose/skeleton estimation accuracy and rapid inference. Thus, it is suitable for safety-critical embedded AI scenarios in autonomous systems, where computational resources are typically limited and fast execution is often required, but accuracy...Show More
Whitening Transformation inspired Self-Attention for Powerline Element Detection
2022 26th International Conference on Pattern Recognition (ICPR)
Year: 2022 | Conference Paper |
Cited by: Papers (4)
Powerline inspection operations involve capturing and inspecting visual footage of powerline elements from elevated positions above and around the powerline and are currently performed with the help of helicopters and/or Unmanned Aerial Vehicles (UAVs). Current technological advances in the areas of robotics and machine learning are towards enabling fully autonomous operations. To this end, one of...Show More
Neural Knowledge Transfer for Sentiment Analysis in Texts with Figurative Language
2022 IEEE 32nd International Workshop on Machine Learning for Signal Processing (MLSP)
Year: 2022 | Conference Paper |
Cited by: Papers (4)
Sentiment analysis in texts, also known as opinion mining, is a significant Natural Language Processing (NLP) task, with many applications in automated social media monitoring, customer feedback processing, e-mail scanning, etc. Despite recent progress due to advances in Deep Neural Networks (DNNs), texts containing figurative language (e.g., sarcasm, irony, metaphors) still pose a challenge to ex...Show More
Properties of Learning Multiplicative Universal Adversarial Perturbations in Image Data
2022 IEEE 32nd International Workshop on Machine Learning for Signal Processing (MLSP)
Year: 2022 | Conference Paper |
Cited by: Papers (1)
Adversarial attacks in image classification are optimization problems that estimate the minimum perturbation required for a single input image, so the neural network misclassifies it. Universal adversarial perturbations are adversarial attacks that target a whole dataset, estimated by e.g., accumulating the perturbations for each image using standard adversarial attacks. This work treats the unive...Show More
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