Smart City Gnosys
Smart city article details
| Title | Yolo-Sifd: Yolo With Sliced Inference And Fractal Dimension Analysis For Improved Fire And Smoke Detection |
|---|---|
| ID_Doc | 62121 |
| Authors | Ishtiaq M.; Won J.-U. |
| Year | 2025 |
| Published | Computers, Materials and Continua, 82, 3 |
| DOI | http://dx.doi.org/10.32604/cmc.2025.061466 |
| Abstract | Fire detection has held stringent importance in computer vision for over half a century. The development of early fire detection strategies is pivotal to the realization of safe and smart cities, inhabitable in the future. However, the development of optimal fire and smoke detection models is hindered by limitations like publicly available datasets, lack of diversity, and class imbalance. In this work, we explore the possible ways forward to overcome these challenges posed by available datasets. We study the impact of a class-balanced dataset to improve the fire detection capability of state-of-the-art (SOTA) vision-based models and propose the use of generative models for data augmentation, as a future work direction. First, a comparative analysis of two prominent object detection architectures, You Only Look Once version 7 (YOLOv7) and YOLOv8 has been carried out using a balanced dataset, where both models have been evaluated across various evaluation metrics including precision, recall, and mean Average Precision (mAP). The results are compared to other recent fire detection models, highlighting the superior performance and efficiency of the proposed YOLOv8 architecture as trained on our balanced dataset. Next, a fractal dimension analysis gives a deeper insight into the repetition of patterns in fire, and the effectiveness of the results has been demonstrated by a windowing-based inference approach. The proposed Slicing-Aided Hyper Inference (SAHI) improves the fire and smoke detection capability of YOLOv8 for real-life applications with a significantly improved mAP performance over a strict confidence threshold. YOLOv8 with SAHI inference gives a mAP:50-95 improvement of more than 25% compared to the base YOLOv8 model. The study also provides insights into future work direction by exploring the potential of generative models like deep convolutional generative adversarial network (DCGAN) and diffusion models like stable diffusion, for data augmentation. © 2025 The Authors. Published by Tech Science Press. |
| Author Keywords | class-balanced dataset; diffusion models; Fire detection; fractal dimension; generative adversarial network (GAN); slicing-aided hyper inference (SAHI); smoke detection; you only look once (YOLO) |
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