Published: Nov. 1, 2024
Language: Английский
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
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
IETE Journal of Research, Journal Year: 2025, Volume and Issue: unknown, P. 1 - 14
Published: Jan. 29, 2025
Language: Английский
Citations
1
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
Advances in computational intelligence and robotics book series, Journal Year: 2025, Volume and Issue: unknown, P. 1 - 30
Published: Feb. 28, 2025
Artificial intelligence has emerged as a key technology for addressing the challenges of urban resilience against disasters. This paper explores how AI can be pivotal in strengthening smart cities, enabling them to withstand and adapt increasingly frequent intense natural disasters that threaten centers worldwide. The research delved into networks make city resilient their interplay. also highlights complementarity between cities frameworks. integration within framework represents significant leap toward building disaster-resilient environments. It emphasizes synergistic relationship frameworks, showcasing incorporation is crucial advancement creating
Language: Английский
Citations
0
SN Computer Science, Journal Year: 2024, Volume and Issue: 5(6)
Published: July 2, 2024
Language: Английский
Citations
2
Journal of Pipeline Science and Engineering, Journal Year: 2024, Volume and Issue: unknown, P. 100246 - 100246
Published: Dec. 1, 2024
Language: Английский
Citations
1
Intelligent Systems with Applications, Journal Year: 2024, Volume and Issue: 25, P. 200463 - 200463
Published: Dec. 7, 2024
Language: Английский
Citations
0
Published: Nov. 1, 2024
Language: Английский
Citations
0