Open Access

ARTICLE

YOLO-SIFD: YOLO with Sliced Inference and Fractal Dimension Analysis for Improved Fire and Smoke Detection

Mariam Ishtiaq^1,2, Jong-Un Won^1,2,*

1 Artificial Intelligence Railroad Research Department, Korea Railroad Research Institute (KRRI), Uiwang, 16105, Republic of Korea
2 Transportation System Engineering, University of Science and Technology (UST), Daejeon, 34113, Republic of Korea

* Corresponding Author: Jong-Un Won. Email:

(This article belongs to the Special Issue: Artificial Intelligence Driven Innovations in Integrating Communications, Image and Signal Processing Applications)

Computers, Materials & Continua 2025, 82(3), 5343-5361. https://doi.org/10.32604/cmc.2025.061466

Received 25 November 2024; Accepted 09 January 2025; Issue published 06 March 2025

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.

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