An Efficient Fire Detection Model Based on Optimized YOLOv5 Leveraging Custom Data Approaches

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dc.contributor.authorEHTASHAM SARFRAZ
dc.date.accessioned2025-08-28T12:16:47Z
dc.date.available2025-08-28T12:16:47Z
dc.date.issued2024
dc.description.abstractFires pose significant threats across various contexts, emphasizing the critical need for accurate and efficient fire detection systems. Deep learning methodologies, particularly YOLO (You Only Look Once), have demonstrated promising capabilities in real-time fire detection applications. In this study, we present an advanced iteration of the YOLOv5s architecture tailored specifically for fire detection tasks. Our approach integrates state-of-the-art techniques to enhance the detection accuracy and efficiency of small-scale fire instances. We introduce novel components, including coordinate attention mechanisms and refined loss functions, to improve the YOLOv5s model's performance to capture intricate fire patterns and accurately identify fire targets.
dc.identifier.urihttps://escholar.umt.edu.pk/handle/123456789/5631
dc.language.isoen
dc.publisherUMT, Lahore
dc.titleAn Efficient Fire Detection Model Based on Optimized YOLOv5 Leveraging Custom Data Approaches
dc.typeThesis
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