Evaluating YOLOv5s and YOLOv8s for Kitchen Fire Detection: A Comparative Analysis DOI Creative Commons
Noor Hassim Ismail, Rizauddin Ramli, Mohd Nizam Ab Rahman

et al.

EMITTER International Journal of Engineering Technology, Journal Year: 2024, Volume and Issue: 12(2), P. 167 - 181

Published: Dec. 27, 2024

Accurate and timely detection of kitchen fires is crucial for enhancing safety reducing potential damage. This paper discusses comparative analysis two cutting-edge object models, YOLOv5s YOLOv8s, focusing on each performance in the critical application fire detection. The these models evaluated using five main key metrics including precision, F1 score, recall, mean Average Precision across various thresholds (mAP50-95) at 50 percent threshold (mAP50). Results indicate that YOLOv8s significantly outperforms several metrics. achieves a recall 0.814 an mAP50 0.897, compared to YOLOv5s' 0.704 0.783. Additionally, attains score 0.861 mAP50-95 0.465, whereas records 0.826 0.342. However, shows higher precision 0.952 YOLOv8s' 0.914. detailed evaluation underscores as more effective model precise settings, highlighting its real-time systems. by offering future work integration sensors with latest YOLO involvement can further optimize efficiency fast rate.

Language: Английский

Hyperparameter optimization of YOLOv8 for smoke and wildfire detection: Implications for agricultural and environmental safety DOI Creative Commons
Leo Ramos, Edmundo Casas, Eduardo Bendek

et al.

Artificial Intelligence in Agriculture, Journal Year: 2024, Volume and Issue: 12, P. 109 - 126

Published: May 31, 2024

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 detailed optimization using One Factor At Time (OFAT) methodology, concentrating key parameters such as learning rate, batch size, weight decay, epochs, optimizer. Insights from OFAT study used to define search spaces subsequent Random Search (RS). final derived RS demonstrated significant improvements over initial model, increasing overall precision by 1.39 %, recall 1.48 F1-score 1.44 [email protected] 0.70 protected]:0.95 5.09 %. We validated enhanced model's efficacy diverse set real-world images, reflecting various settings, confirm robustness in detecting fire. These results underscore reliability effectiveness scenarios critical safety work, representing advancement field fire through machine learning, lays strong foundation future research solutions aimed at safeguarding areas natural environments.

Language: Английский

Citations

13
A comparative study of YOLOv5 and YOLOv8 for corrosion segmentation tasks in metal surfaces DOI Creative Commons
Edmundo Casas, Leo Ramos, Cristian Romero

et al.

Array, Journal Year: 2024, Volume and Issue: 22, P. 100351 - 100351

Published: June 1, 2024

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 further corroborated by evaluations. These assessments were instrumental emphasizing proficiency handling complex corroded surfaces. However, largest dataset, both models encountered challenges, particularly with overlapping bounding boxes. notably lagged, struggling achieve performance standards set YOLOv8, especially irregular In conclusion, our findings underscore enhanced capabilities, establishing it as preferable choice for real-world detection research thus offers invaluable insights, poised redefine management strategies guide future explorations identification.

Language: Английский

Citations

13
Computer vision for wildfire detection: a critical brief review DOI
Leo Ramos, Edmundo Casas, Eduardo Bendek

et al.

Multimedia Tools and Applications, Journal Year: 2024, Volume and Issue: 83(35), P. 83427 - 83470

Published: March 13, 2024

Language: Английский

Citations

9
Multispectral Semantic Segmentation for Land Cover Classification: An Overview DOI Creative Commons
Leo Ramos, Ángel D. Sappa

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Journal Year: 2024, Volume and Issue: 17, P. 14295 - 14336

Published: Jan. 1, 2024

Land cover classification (LCC) is a process used to categorize the Earth's surface into distinct land types. This vital for environmental conservation, urban planning, agricultural management, and climate change research, providing essential data sustainable decision-making. The use of multispectral imaging (MSI), which captures beyond visible spectrum, has emerged as one most utilized image modalities addressing this task. Additionally, semantic segmentation techniques play role in domain, enabling precise delineation labeling classes within imagery. integration these three concepts given rise an intriguing ever-evolving research field, witnessing continuous advancements aimed at enhancing (MSSS) methods LCC. Given dynamic nature there need thorough examination latest trends understand its evolving landscape. Therefore, paper presents review current aspects field MSSS LCC, following key points: (1) prevalent datasets acquisition methods, (2) preprocessing managing MSI data, (3) typical metrics evaluation criteria assessing performance (4) methodologies employed, (5) spectral bands spectrum commonly utilized. Through analysis, our objective provide valuable insights state contributing ongoing development understanding while also perspectives future directions.

Language: Английский

Citations

6
An End-to-End Platform for Managing Third-Party Risks in Oil Pipelines DOI Creative Commons
Edmundo Casas, Leo Ramos, Cristian Romero

et al.

IEEE Access, Journal Year: 2024, Volume and Issue: 12, P. 77831 - 77851

Published: Jan. 1, 2024

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 models in real-world operational environments. For this, we construct dataset comprising 1,003 aerial images, covering seven classes vehicles commonly encountered pipelines, including trucks, forklifts, machinery, pickups, tractors, vehicles, buses. This serves as foundation training hyperparameter optimization YOLOv8x-based detection model, used work. The optimized model exhibits strong performance across precision, recall, F1-score, mean average precision metrics compared baseline model. Additionally, graphical tests illustrated that demonstrates higher confidence scores reduction false positives. addition, platform has been developed seamlessly integrate offers range functionalities, enabling users access alert history, prioritize alerts, track actions taken on each alert, visualize alerts geographically, receive notifications identified risks, generate detailed reports comprehensive analysis decision-making.

Language: Английский

Citations

5
A Comprehensive Experimental Liquid‐Level Control System for Advancing Fault Diagnosis Research Innovation: Data, Models, and Procedures DOI Creative Commons

Hilina Workneh,

Ioannis A. Raptis

Advanced Control for Applications, Journal Year: 2025, Volume and Issue: 7(2)

Published: April 20, 2025

ABSTRACT This work addresses the development of a laboratory benchmark system designed for testing and comparing model‐based fault diagnosis algorithms. We selected liquid‐level control with three interconnected storage tanks as physical process. provide detailed description first‐principles mathematical modeling deriving state‐space equations System identification was performed using elementary least squares to estimate model parameters from input/output data. The primary contribution this paper is presentation an open‐access repository containing extensive sensor actuator data experiments on process experiencing faults. enables researchers validate their algorithms sensory real‐world subjected realistic uncertainty measurement challenges. validation identified dynamic its agreement collected demonstrate capabilities proposed detection

Language: Английский

Citations

0
A review of computer vision applications for asset inspection in the oil and gas Industry DOI Creative Commons
Edmundo Casas, Leo Ramos, Cristian Romero

et al.

Journal of Pipeline Science and Engineering, Journal Year: 2024, Volume and Issue: unknown, P. 100246 - 100246

Published: Dec. 1, 2024

Language: Английский

Citations

1
Continual learning, deep reinforcement learning, and microcircuits: a novel method for clever game playing DOI
Oscar Chang, Leo Ramos, Manuel Eugenio Morocho-Cayamcela

et al.

Multimedia Tools and Applications, Journal Year: 2024, Volume and Issue: unknown

Published: April 15, 2024

Language: Английский

Citations

0
Synthetic generated data for intelligent corrosion classification in oil and gas pipelines DOI Creative Commons
Leo Ramos, Edmundo Casas, Francklin Rivas

et al.

Intelligent Systems with Applications, Journal Year: 2024, Volume and Issue: 25, P. 200463 - 200463

Published: Dec. 7, 2024

Language: Английский

Citations

0
Evaluating YOLOv5s and YOLOv8s for Kitchen Fire Detection: A Comparative Analysis DOI Creative Commons
Noor Hassim Ismail, Rizauddin Ramli, Mohd Nizam Ab Rahman

et al.

EMITTER International Journal of Engineering Technology, Journal Year: 2024, Volume and Issue: 12(2), P. 167 - 181

Published: Dec. 27, 2024

Accurate and timely detection of kitchen fires is crucial for enhancing safety reducing potential damage. This paper discusses comparative analysis two cutting-edge object models, YOLOv5s YOLOv8s, focusing on each performance in the critical application fire detection. The these models evaluated using five main key metrics including precision, F1 score, recall, mean Average Precision across various thresholds (mAP50-95) at 50 percent threshold (mAP50). Results indicate that YOLOv8s significantly outperforms several metrics. achieves a recall 0.814 an mAP50 0.897, compared to YOLOv5s' 0.704 0.783. Additionally, attains score 0.861 mAP50-95 0.465, whereas records 0.826 0.342. However, shows higher precision 0.952 YOLOv8s' 0.914. detailed evaluation underscores as more effective model precise settings, highlighting its real-time systems. by offering future work integration sensors with latest YOLO involvement can further optimize efficiency fast rate.

Language: Английский

Citations

0