The project amalgamates sophisticated computer vision techniques with a streamlined deployment approach to deliver a robust fire and smoke detection system. By exploiting the YOLOv5 algorithm for precise object detection and embracing Streamlit for user interaction, the project presents a holistic solution for prompt identification of potential fire-related hazards. The implications span various sectors, promising elevated safety measures and proactive mitigation of fire-related risks.
The YOLOv5 architecture embodies efficiency and effectiveness in object detection. It follows a modular design, comprising a backbone, neck, and head components. The backbone extracts hierarchical features from input images, capturing essential visual information. The neck refines these features, enhancing their relevance to detection tasks. Finally, the head predicts bounding box coordinates and class probabilities based on these features.
The Results section provides a comprehensive overview of the outcomes of your research, including quantitative performance metrics, visual examples of detections.
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Lakshmi Mani Shankar
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Krishna Pradeep
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Sujith Kumar
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Swarup Yakkala
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