ENHANCING FIRE DETECTION WITH YOLOV10: ADVANCED TECHNIQUES FOR FLAME AND SMOKE RECOGNITION

Abstract

This project introduces an advanced method for detecting flames and smoke using the YOLOv10 algorithm, aimed at improving fire detection systems for enhanced safety and prevention. The proposed system incorporates significant advancements to address challenges such as cluttered backgrounds, low visibility, varying fire intensities, and overlapping objects. By leveraging an enhanced feature extraction mechanism, the system captures finer details of flames and smoke. It also includes a sophisticated attention mechanism to prioritize critical fire-related areas in an image while suppressing irrelevant background information. To further enhance detection accuracy, the system uses an improved bounding box regression method, ensuring precise localization of flames and smoke, even in dense or challenging environments. These improvements make the system more robust, adaptable, and capable of handling high-resolution images and videos efficiently. Experimental results demonstrate that the YOLOv10-based system significantly outperforms previous methods, such as YOLOv5s, in terms of precision, recall, and mean average precision (mAP). Additionally, the system processes data in real-time, making it suitable for largescale applications in industries, public spaces, and residential areas. By ensuring early and reliable detection of flames and smoke, the proposed system offers a practical solution for improving fire safety and
prevention measures.