Optimizing hyperparameters of YOLOv10 for arson detection using advanced optimization algorithms

Arson detection plays a critical role in protecting lives and property in high-risk environments such as airports, industrial zones, and other public areas. Recent advances in deep learning (DL), particularly YOLOv10, have demonstrated strong potential in real-time object detection. However, the mod...

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Veröffentlicht in:	PloS one Jg. 20; H. 10; S. e0333374
Hauptverfasser:	Abbod, Ali Abbas, Abdulmunim, Matheel E., Mageed, Ismail A.
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	United States Public Library of Science 07.10.2025 Public Library of Science (PLoS)
Schlagworte:	Accuracy Airports Algorithms Analysis Arson Bears Data augmentation Datasets Deep Learning Efficiency Heuristic methods Humans Identification and classification Innovations Machine learning Mathematical optimization Optimization Optimization algorithms Optimization techniques Real time Safety critical Surveillance Surveillance systems Iraq
ISSN:	1932-6203, 1932-6203
Online-Zugang:	Volltext
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