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International Conference on Technological Innovation and AI Research (ICTIAIR 2025)

作者： Ariana Deyaneira Jiménez-Narváez Jonathan Javier Loor-Duque Diego Josue Andrade-Pelaez Manuel Eugenio Morocho-Cayamcela Raisa Torres-Ramírez Deep Learning for Autonomous Driving Robotics and Computer Vision Research Group (DeepARC) School of Mathematical and Computational Sciences Yachay Tech University San Miguel de Urcuquí Ecuador Applied Geology and Research Group School of Geological Sciences Energy and Environment Yachay Tech University San Miguel de Urcuquí Ecuador

ISBN: (数字)9781837243235

Floods are among the most frequent and devastating natural disasters, significantly impacting urban and rural communities worldwide. Achachay App is a mobile application designed to enhance flood data collection and analysis through direct community engagement. The app enables users to report real-time flood events by sharing detailed descriptions, geographic coordinates, and multimedia evidence such as photos or videos. Researchers use these community-sourced data to analyze and reconstruct watercourse behaviors that lead to flooding. Ecuador was selected for implementation as a pilot initiative, where the app successfully mapped flood-prone areas and secondary flows. These features aim to deepen understanding of the interplay between geological and climatic phenomena, supporting disaster preparedness and prevention. By bridging technology and community collaboration, the Achachay Appoffers valuable insights for policymakers to design sustainable public policies that mitigate risks and promote social and economic development in Ecuador.

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Learning Multi-dimensional Edge Feature-based AU Relation Graph for Facial Action Unit Recognition

arXiv

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arXiv 2022年

作者： Luo, Cheng Song, Siyang Xie, Weicheng Shen, Linlin Gunes, Hatice Computer Vision Institute Shenzhen University China Shenzhen Institute of Artificial Intelligence and Robotics for Society China Guangdong Key Laboratory of Intelligent Information Processing China Department of Computer Science and Technology University of Cambridge United Kingdom

The activations of Facial Action Units (AUs) mutually influence one another. While the relationship between a pair of AUs can be complex and unique, existing approaches fail to specifically and explicitly represent such cues for each pair of AUs in each facial display. This paper proposes an AU relationship modelling approach that deep learns a unique graph to explicitly describe the relationship between each pair of AUs of the target facial display. Our approach first encodes each AU’s activation status and its association with other AUs into a node feature. Then, it learns a pair of multi-dimensional edge features to describe multiple task-specific relationship cues between each pair of AUs. During both node and edge feature learning, our approach also considers the influence of the unique facial display on AUs’ relationship by taking the full face representation as an input. Experimental results on BP4D and DISFA datasets show that both node and edge feature learning modules provide large performance improvements for CNN and transformer-based backbones, with our best systems achieving the state-of-the-art AU recognition results. Our approach not only has a strong capability in modelling relationship cues for AU recognition but also can be easily incorporated into various backbones. Our PyTorch code is made available at https://***/CVI-SZU/ME-GraphAU. Copyright © 2022, The Authors. All rights reserved.

关键词： Chemical activation

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Motion-X++: A Large-Scale Multimodal 3D Whole-body Human Motion Dataset

arXiv

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arXiv 2025年

作者： Zhang, Yuhong Lin, Jing Zeng, Ailing Wu, Guanlin Lu, Shunlin Fu, Yurong Cai, Yuanhao Zhang, Ruimao Wang, Haoqian Zhang, Lei Shenzhen International Graduate School Tsinghua University Guangdong 518000 China Department of Computer Vision and Robotics IDEA Guangdong 518000 China Whiting School of Engineering Johns Hopkins University MD21218 United States School of Data Science The Chinese University of Hong Kong Guangdong GD Shenzhen518000 China

In this paper, we introduce Motion-X++, a large-scale multimodal 3D expressive whole-body human motion dataset. Existing motion datasets predominantly capture body-only poses, lacking facial expressions, hand gestures, and fine-grained pose descriptions, and are typically limited to lab settings with manually labeled text descriptions, thereby restricting their scalability. To address this issue, we develop a scalable annotation pipeline that can automatically capture 3D whole-body human motion and comprehensive textural labels from RGB videos and build the Motion-X dataset comprising 81.1K text-motion pairs. Furthermore, we extend Motion-X into Motion-X++ by improving the annotation pipeline, introducing more data modalities, and scaling up the data quantities. Motion-X++ provides 19.5M 3D whole-body pose annotations covering 120.5K motion sequences from massive scenes, 80.8K RGB videos, 45.3K audios, 19.5M frame-level whole-body pose descriptions, and 120.5K sequence-level semantic labels. Comprehensive experiments validate the accuracy of our annotation pipeline and highlight Motion-X++’s significant benefits for generating expressive, precise, and natural motion with paired multimodal labels supporting several downstream tasks, including text-driven whole-body motion generation, audio-driven motion generation, 3D whole-body human mesh recovery, and 2D whole-body keypoints estimation, etc. Copyright © 2025, The Authors. All rights reserved.

关键词： Semantics

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Spiral coverage path planning for Multi-UAV photovoltaic panel inspection applications

Spiral coverage path planning for Multi-UAV photovoltaic pan...

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International Conference on Unmanned Aircraft Systems (ICUAS)

作者： Marco Andrés Luna Mohammad Sadeq Ale Isaac Miguel Fernandez-Cortizas Carlos Santos Ahmed Refaat Ragab Martin Molina Pascual Campoy Computer Vision and Aerial Robotics Group (CVAR) Centre for Automation and Robotics (C.A.R.) Universidad Politécnica de Madrid (UPM-CSIC) Madrid Spain Department of Signal Theory and Communications University of Alcala (UAH) Madrid Spain Department of Network Faculty of Information Systems and Computer Science October 6 University Giza Egypt Department of Artificial Intelligence Universidad Politécnica de Madrid Madrid Spain

This paper deals with the problem of coverage path planning for multiple UAVs in disjoint regions. For this purpose, a spiral-coverage path planning algorithm is proposed. Additionally, task assignment methods for multi-region inspection with a swarm of UAVs are applied. The centralized system architecture is described, and an adaptive sliding mode controller is designed. Furthermore, we evaluate the performance of the proposed techniques by obtaining numerical results and simulations with the controller. The results show that the spiral pattern optimizes the cost of the mission and improves the task distribution of the mission planning system. Additionally, the performance of the proposed controller is robust to simulated disturbances.

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Maize EfficientNet Fusion: Advancing Maize Disease Detection with MF-NET

Maize EfficientNet Fusion: Advancing Maize Disease Detection...

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Proceedings of the Digital Image Computing: Technqiues and Applications (DICTA)

作者： Fatima Khalid Muhammad Hanif Qurat Ul Ain Faculty of Computer Science and Engineering Ghulam Ishaq Khan Institute of Engineering Sciences and Engineering Topi KPK Pakistan Aerial Robotics and Vision Lab Ghulam Ishaq Khan Institute of Engineering Sciences and Engineering Topi KPK Pakistan Department of Computing Shifa Tameer-E-Millat University Islamabad Pakistan

ISBN: (数字)9798350379037

ISBN: (纸本)9798350379044

Maize is a vital global crop, essential for food security but highly susceptible to diseases that threaten yield and quality. Traditional methods for detecting these diseases are computationally intensive and rely on high-quality images, limiting their practical use in diverse field conditions. This research addresses these challenges by proposing MF-Net, a novel model that leverages two EfficientNet-b0 architectures with ReLU and Swish activation functions. Our approach involves partially training Model R with frozen early layers using ReLU, and fully training Model S with Swish activation for enhanced optimization and robustness. We employ model concatenation and feature fusion techniques to create a lightweight yet powerful model, complemented by additional layers for improved feature extraction and regularization. Experimental results on the Corn Disease and Severity (CD&S) dataset are compelling: MF-Net achieves a remarkable 95.7% accuracy and 96% precision in disease classification, and 88.3% accuracy and 88.46% precision in severity level detection. These results highlight the model's effectiveness under challenging conditions such as cluttered backgrounds and variable lighting, significantly reducing computation time without compromising accuracy. Hence, MF-Net presents an innovative and efficient solution for maize disease detection and severity assessment, offering significant advancements for agricultural practices and crop management.

关键词： Training Adaptation models Accuracy Computational modeling Crops Lighting computer architecture Feature extraction Robustness Diseases

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Data Engineering and AI-Powered Skin Cancer Identification for Healthcare Applications

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Procedia computer Science 2024年 246卷 179-188页

作者： Imran Ahmed Misbah Ahmad Abdellah Chehri Gwanggil Jeon School of Computing and Information Science Anglia Ruskin University Cambridge UK Animal and Agriculture Department Hartpury University Gloucester UK Centre for Machine Vision Bristol Robotics Laboratory University of the West of England Bristol UK Department of Mathematics and Computer Science at the Royal Military College of Canada (RMC) Canada Department of Embedded Systems Engineering Incheon National University Incheon Korea

Skin cancer diagnosis, a critical task in the medical domain, can be revolutionized through the application of advanced deep-learning techniques. This work investigates the efficacy of Convolutional Neural Networks (CNNs) in the automated classification of skin cancer. The process begins with a comprehensive explanation of key CNN layers: Conv2D, MaxPool2D, Dropout, and Dense. The Conv2D layers employ learnable filters that transform localized image segments, while MaxPool2D contributes to downsampling, effectively reducing computational cost and overfitting risk. Integrating these layers enables the network to capture local and global characteristics, which is crucial for accurate classification. Adding Dropout layers enhances generalization and mitigates overfitting by introducing randomness during training. ReLU activation functions infuse non-linearity, and the Flatten layer facilitates the transition to fully connected layers. The proposed CNN architecture is meticulously designed considering filter counts, kernel sizes, and pooling dimensions. The trained model demonstrates promising performance by utilizing the HAM10000 dataset, encompassing diverse skin lesion images across seven classes. The CNN model’s parameters and architecture are systematically presented, offering insights into its design rationale. The model undergoes optimization with the Adam optimizer and annealing techniques to facilitate convergence. The model’s effectiveness is evaluated on validation and test datasets, demonstrating an accuracy of 78.55% and 76.49%, respectively, for skin cancer classification. Data augmentation strategies are introduced to enhance model generalization further. The results underscore CNN’s potential as a robust tool for automating skin cancer diagnosis, aligning with the broader trend of leveraging deep learning for medical image analysis

关键词： Image Processing Medical Imaging Deep learning Skin Cancer Data Analytics

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Inspecting Mega Solar Plants through computer vision and Drone Technologies

Inspecting Mega Solar Plants through Computer Vision and Dro...

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Frontiers of Information Technology (FIT)

作者： Syed Umaid Ahmed Muhammad Affan Muhammad Ilyas Raza Muhammad Harris Hashmi Department of Computer Sciences National University of Computer and Emerging Sciences Karachi Pakistan SeaTech School of Engineering University of Toulon La Garde France Institute of Robotics and Computer Vision Innopolis University Innopolis Russia Department of Computer Science University of Alabama at Birmingham Birmingham United States

ISBN: (纸本)9798350345940

This research presents a unique approach for monitoring the large-scale grid-connected photovoltaic modules in solar power plants using state-of-art object detection YOLOv5 algorithm and classical image processing techniques. We have highlighted an integral part of the fully automated system in which a drone takes flight over the solar park and shoot the videos. Videos are preprocessed and used for trained YOLOv5 model to recognize the clean and dirty panels. The process is defined for a selected site and can be implemented using a Raspberry Pi. This system processes the images taken by drones, generates a report, and sends it to the concerned department automatically via email every day so that timely maintenance can be done for the long-life and safe operation of solar arrays. The inspection timeline for the same process was about one hundred and twenty hours, reduced to five minutes. It means that 99.93% of the time is saved through vision and robust automation techniques.

关键词： Photovoltaic systems computer vision Image processing Object detection Maintenance engineering Inspection Information technology

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NDD: A 3D Point Cloud Descriptor Based on Normal Distribution for Loop Closure Detection

arXiv

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arXiv 2022年

作者： Zhou, Ruihao He, Li Zhang, Hong Lin, Xubin Guan, Yisheng The Department of Electromechanical Engineering Guangdong University of Technology China The Department of Electronic and Electrical Engineering Southern University of Science and Technology China Shenzhen Key Laboratory of Robotics and Computer Vision China

Loop closure detection is a key technology for long-term robot navigation in complex environments. In this paper, we present a global descriptor, named Normal Distribution Descriptor (NDD), for 3D point cloud loop closure detection. The descriptor encodes both the probability density score and entropy of a point cloud as the descriptor. We also propose a fast rotation alignment process and use correlation coefficient as the similarity between descriptors. Experimental results show that our approach outperforms the state-of-the-art point cloud descriptors in both accuracy and efficency. The source code is available and can be integrated into existing LiDAR odometry and mapping (LOAM) systems. © 2022, CC BY.

关键词： Normal distribution

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UniTSFace: unified threshold integrated sample-to-sample loss for face recognition 23

UniTSFace: unified threshold integrated sample-to-sample los...

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Proceedings of the 37th International Conference on Neural Information Processing Systems

作者： Qiufu Li Xi Jia Jiancan Zhou Linlin Shen Jinming Duan National Engineering Laboratory for Big Data System Computing Technology Shenzhen University China and Computer Vision Institute Shenzhen University China and SZU Branch Shenzhen Institute of Artificial Intelligence and Robotics for Society China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University China and Computer Vision Institute Shenzhen University China and School of Computer Science University of Birmingham UK National Engineering Laboratory for Big Data System Computing Technology Shenzhen University China and Computer Vision Institute Shenzhen University China and Aqara Lumi United Technology Co. Ltd China School of Computer Science University of Birmingham UK and Alan Turing Institute UK

Sample-to-class-based face recognition models can not fully explore the cross-sample relationship among large amounts of facial images, while sample-to-sample-based models require sophisticated pairing processes for training. Furthermore, neither method satisfies the requirements of real-world face verification applications, which expect a unified threshold separating positive from negative facial pairs. In this paper, we propose a unified threshold integrated sample-to-sample based loss (USS loss), which features an explicit unified threshold for distinguishing positive from negative pairs. Inspired by our USS loss, we also derive the sample-to-sample based softmax and BCE losses, and discuss their relationship. Extensive evaluation on multiple benchmark datasets, including MFR, IJB-C, LFW, CFP-FP, AgeDB, and MegaFace, demonstrates that the proposed USS loss is highly efficient and can work seamlessly with sample-to-class-based losses. The embedded loss (USS and sample-to-class Softmax loss) overcomes the pitfalls of previous approaches and the trained facial model UniTSFace exhibits exceptional performance, outperforming state-of-the-art methods, such as CosFace, ArcFace, VPL, AnchorFace, and UNPG. Our code is available at https://***/CVI-SZU/UniTSFace.

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Deep Learning vs. Traditional 3d Registration: A Featureless 3d Registration Baseline

SSRN

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SSRN 2023年

作者： Bojanic, David Bartol, Kristijan Forest, Josep Petkovic, Tomislav Pribanic, Tomislav University of Zagreb Faculty of Electrical Engineering and Computing Unska 3 Zagreb10000 Croatia TU Dresden Dresden01069 Germany University of Girona Computer Vision and Robotics Research Institute Plaça de Sant Domènec 3 Girona17004 Spain

Recent 3D registration methods are mostly learning-based that either find correspondences in feature space and match them, or directly estimate the registration transformation from the given point cloud features. Therefore, these feature based methods have difficulties with generalizing onto point clouds that differ substantially from their training data. We propose a simple featureless traditional 3D registration baseline method based on the weighted cross-correlation between two given point clouds. Our method achieves strong results on current benchmarking datasets, outperforming most deep-learning methods. We further propose a methodology to create a 3D registration benchmark, given a point cloud dataset, that provides a more informative evaluation of a method w.r.t. other benchmarks. We also propose to use the 3D registration parameter range of a benchmark as an indicator of its difficulty. Using this methodology, we create a novel FAUST-partial (FP) benchmark, based on the FAUST dataset, with several difficulty levels. The FP benchmark addresses the limitations of the current benchmarks: lack of data and parameter range variability, and allows to evaluate the strengths and weaknesses of a 3D registration method w.r.t. a single registration parameter. Using the new FP benchmark, we provide a thorough analysis of the current state-of-the-art methods. Our source code is available on GitHub. © 2023, The Authors. All rights reserved.

关键词： Deep learning

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