A Vision-Based Approach for Real Time Fire and Smoke Detection Using FASDD

Abstract

Fire and smoke detection is essential in safety-critical environments, yet traditional systems often struggle with maintaining accuracy and reducing false alarms in complex scenarios. Therefore, vision-based systems are used for preventing fire tragedies. There are different machines and deep learning techniques used to timely and effectively detect the fire/smoke and one of them is “You Only Look Once” (Yolo). Yolo is a type of neural network (CNN), which is good at detecting patterns in images. Yolov8 is the most widely used object detection model for vision-based systems. However, there still exist some challenges, such as high computational complexity and low detection performance. This study introduces a novel lightweight and optimal Yolov8 model to over these challenges. To enhance performance, Efficient Channel Attention (ECA) is integrated into the model’s head to focus on critical features, while the C3Ghost module in the backbone reduces computational overhead without sacrificing accuracy. The model is trained and evaluated on two datasets: FS and FASDD comprising diverse indoor, outdoor fire and smoke scenarios and has achieved a mAP@50 of 89%, precision:88%, recall: 84%, and an F1-score of 86.4% which shows an improvement of 4.56% in precision, 2% of recall and 8.10% in mAP@50 in comparison with the existing state of the art. Our findings have demonstrated significant improvements in detection accuracy and false-positive reduction compared to other computationally intensive models like Yolov5, Yolov7 and (vision) Transformers. Our model is lightweight architecture, more accurate in fire, and smoke detection, and makes it suitable for embedded device deployment.