A5053320427- Fire Detection and Safety Systems
- Video Surveillance and Tracking Methods
- Fire effects on ecosystems
- Industrial Vision Systems and Defect Detection
- Non-Destructive Testing Techniques
- Image Enhancement Techniques
- Infrastructure Maintenance and Monitoring
- Structural Integrity and Reliability Analysis
- Advanced Neural Network Applications
- Multimodal Machine Learning Applications
- Advanced Measurement and Detection Methods
- Advanced Image and Video Retrieval Techniques
- Thermography and Photoacoustic Techniques
- Diverse Approaches in Healthcare and Education Studies
- Impact of AI and Big Data on Business and Society
- Environmental Sustainability in Business
- Image and Object Detection Techniques
- Oil and Gas Production Techniques
- Material Properties and Failure Mechanisms
- Ecology and Conservation Studies
- Drilling and Well Engineering
- Reservoir Engineering and Simulation Methods
- Fashion and Cultural Textiles
Universidad de Deusto
2024
Menlo School
2024
CHI Health Immanuel
2023
Pontificia Universidad Católica del Ecuador
2023
This paper presents a comprehensive evaluation of various YOLO architectures for smoke and wildfire detection, including YOLOv5, YOLOv6, YOLOv7, YOLOv8, YOLO-NAS. The study aims to assess their effectiveness in early detection wildfires using the Foggia dataset, comprising 8,974 images specifically designed this purpose. Performance employs metrics such as Recall, Precision, F1-score, mean Average Precision provide holistic assessment models' performance. follows rigorous methodology...
This paper provides a thorough analysis and comparison of the YOLOv5 YOLOv8 models for wildfire smoke detection, using Foggia dataset evaluation. The study examines small (s), medium (m), large (l) variants each architecture employs various metrics, including recall, precision, F1-Score, mAP@50, to assess performance. Additional considerations such as training inference times, along with number epochs required optimal are also evaluated gauge models’ real-world efficiency effectiveness....
This study explores the effectiveness of ConvNeXt model, an advanced computer vision architecture, in task image captioning.We integrated with a Long Short-Term Memory network that includes visual attention module, focusing on assessing its performance across different scenarios.Experiments were conducted using various versions for feature extraction, learning rates during training phase tested, and impact including or excluding teacherforcing was analyzed.The MS COCO 2014 dataset employed,...
This study delves into the comparative efficacy of YOLOv5 and YOLOv8 in corrosion segmentation tasks. We employed three unique datasets, comprising 4942, 5501, 6136 images, aiming to thoroughly evaluate models' adaptability robustness diverse scenarios. The assessment metrics included precision, recall, F1-score, mean average precision. Furthermore, graphical tests offered a visual perspective on capabilities each architecture. Our results highlight YOLOv8's superior speed accuracy across...
In this study, we extensively evaluated the viability of state-of-the-art YOLOv8 architecture for object detection tasks, specifically tailored smoke and wildfire identification with a focus on agricultural environmental safety. All available versions were initially fine-tuned domain-specific dataset that included variety scenarios, crucial comprehensive monitoring. The 'large' version (YOLOv8l) was selected further hyperparameter tuning based its performance metrics. This model underwent...
Ensuring the safe and reliable operation of underground oil pipelines is crucial to prevent environmental disasters maintain uninterrupted energy supply. Yet, this vast network faces threats from third-party activities, natural disasters, aging infrastructure, posing risks catastrophic consequences if left unaddressed. In response need, paper presents a computer vision system for detecting (vehicular movement) near pipelines. Our primary objective showcase practical application cutting-edge...
Abstract The energy transition presents a critical juncture for the oil and gas industry. As we navigate toward more sustainable future, digital oilfield (DOF) technologies play pivotal role in optimizing operations, reducing environmental impact, ensuring security. This paper aims to explore best practices lessons learned from two decades of implementations. scope includes analyzing successful case studies, challenges faced, practical guidelines short-term DOF adoption context transition....