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Works69 from public data
- Drone Deep Reinforcement Learning: A Review309
The state of the art of one subset of these algorithms: the deep reinforcement learning (DRL) techniques, designed to ensure stable and smooth UAV navigation by training computer-simulated environments are described.
- Car Detection using Unmanned Aerial Vehicles: Comparison between Faster R-CNN and YOLOv3305
This paper investigates the performance of two state-of-the art CNN algorithms, namely Faster R-CNN and YOLOv3, in the context of car detection from aerial images, and demonstrates in this paper that Yolov3 outperforms FasterR-CNN in sensitivity and processing time, although they are comparable in the precision metric.
- Deep Learning Approach for Car Detection in UAV Imagery296
An automatic solution to the problem of detecting and counting cars in unmanned aerial vehicle (UAV) images that outperforms the state-of-the-art methods, both in terms of accuracy and computational time.
- Vision Transformers in Image Restoration: A Survey148
It is noted that incorporating ViT in the new architectures for image restoration is becoming a rule, due to some advantages compared to CNN, such as better efficiency, especially when more data are fed to the network, robustness in feature extraction, and a better feature learning approach that sees better the variances and characteristics of the input.
- Convolutional Neural Networks for Electrocardiogram Classification123
This approach relies on a deep convolutional neural network pretrained on an auxiliary domain with very large labelled images coupled with an additional network composed of fully connected layers to achieve better results for the detection of ventricular ectopic beats and supraventricular ectopy beats compared to state-of-the-art methods.
- Vehicle Detection from Aerial Images Using Deep Learning: A Comparative Study104
This paper assesses the performance of three state-of-the-art CNN algorithms, namely Faster R-CNN, which is the most popular region-based algorithm, as well as Y OLOv3 and YOLOv4, which are known to be the fastest detection algorithms.
- A Novel Hybrid Machine Learning Classification for the Detection of Bruxism Patients Using Physiological Signals73
The results show that more accurate bruxism detection is achieved by exploiting the electroencephalogram signal (C4-P4) and the present work can be applied for home monitoring systems for bruXism detection.
- A Multi-Stage Deep-Learning-Based Vehicle and License Plate Recognition System with Real-Time Edge Inference71
A novel multi-stage, real-time, deep learning-based vehicle identification and license plate recognition system based on a set of algorithms that efficiently integrate two object detectors, an image classifier, and a multi-object tracker to recognize car models and license plates.
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- TAU: A framework for video-based traffic analytics leveraging artificial intelligence and unmanned aerial systems55
This paper uses the use of Artificial Intelligence (AI) and Unmanned Aerial Vehicles (UAVs) to develop an AI-integrated video analytics framework, called TAU (Traffic Analysis from UAVs), for automated traffic analytics and understanding.
- TESR: Two-Stage Approach for Enhancement and Super-Resolution of Remote Sensing Images54
A new architecture called TESR (Two-stage approach for Enhancement and super-resolution), leveraging the power of Vision Transformers and the Diffusion Model to increase the resolution of RS images artificially, which outperforms state-of-the-art architectures on super- Resolution of remote sensing images on the UCMerced benchmark dataset.
- COVID-19 Diagnosis in Chest X-rays Using Deep Learning and Majority Voting47
This paper finds that using the Majority Voting approach is an adequate strategy to adopt in general cases for this task and may achieve an average accuracy up to 99.314%.
- Data-Efficient Domain Adaptation for Semantic Segmentation of Aerial Imagery Using Generative Adversarial Networks45
This paper proposed a new algorithm that uses Generative Adversarial Networks (GAN) architecture to minimize the domain shift and increase the ability of the model to work on new targeted domains, and proposed a methodology to use these labeled data efficiently.
- A Machine Learning Approach Involving Functional Connectivity Features to Classify Rest-EEG Psychogenic Non-Epileptic Seizures from Healthy Controls44
An intrinsic organization of functional brain networks that reflects a dysfunctional level of integration across brain regions, which can provide new insights into the pathophysiological mechanisms of PNES is hypothesized.
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- Early detection of red palm weevil infestations using deep learning classification of acoustic signals39
The proposed approach is based on RPW sound activities being recorded and analyzed and outperformed existing techniques for public datasets for early detection of RPW.
- A Hybrid Privacy-Preserving Deep Learning Approach for Object Classification in Very High-Resolution Satellite Images35
A hybrid PPDL approach for object classification for very-high-resolution satellite images that combines Paillier homomorphic encryption (PHE) and somewhat homomorphicryption (SHE) while ensuring a good runtime and high object classification accuracy is proposed.
- Helping the Visually Impaired See via Image Multi-labeling Based on SqueezeNet CNN35
The proposed deep CNN is an effective solution for predicting the presence of objects in a scene and can be successfully used as a module within BlindSys, a larger system that assists the visually impaired in an indoor environment.
- The Tenth NTIRE 2025 Image Denoising Challenge Report34
An overview of the NTIRE 2025 Image Denoising Challenge is presented, highlighting the proposed methodologies and corresponding results, providing insights into the current state-of-the-art in image denoising.
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- Activity Monitoring of Islamic Prayer (Salat) Postures using Deep Learning32
This work addresses the problem of the recognition of the basic gestures of Islamic prayer using Convolutional Neural Networks (CNN), and is the first work that addresses human activity recognition of Salat using deep learning.
- NTIRE 2026 Challenge on Robust AI-Generated Image Detection in the Wild29
This report provides a comprehensive overview of the NTIRE 2026 Challenge on Robust AI-Generated Image Detection in the Wild, describes the proposed solutions, and can be used as a valuable reference for researchers and practitioners in increasing the robustness of the detection models to real-world transformations.
- NTIRE 2025 Challenge on Low Light Image Enhancement: Methods and Results29
This paper thoroughly evaluates the state-of-the-art advancements in LLIE, showcasing the significant progress.
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Publication data from OpenAlex, with missing venues and authors filled in from Crossref; citation counts are the higher of OpenAlex and Semantic Scholar, last synced 2026-10-14. One-sentence summaries under some papers are written by Semantic Scholar’s model. Citation counts may be lower than on Google Scholar, which indexes more sources.
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