Kemahyanto Exaudi^1,2, Deris Stiawan^3,*, Bhakti Yudho Suprapto¹, Hanif Fakhrurroja⁴, Mohd. Yazid Idris⁵, Tami A. Alghamdi⁶, Rahmat Budiarto⁶

CMC-Computers, Materials & Continua, Vol.86, No.1, pp. 1-24, 2026, DOI:10.32604/cmc.2025.069377 - 10 November 2025

Abstract Sudden wildfires cause significant global ecological damage. While satellite imagery has advanced early fire detection and mitigation, image-based systems face limitations including high false alarm rates, visual obstructions, and substantial computational demands, especially in complex forest terrains. To address these challenges, this study proposes a novel forest fire detection model utilizing audio classification and machine learning. We developed an audio-based pipeline using real-world environmental sound recordings. Sounds were converted into Mel-spectrograms and classified via a Convolutional Neural Network (CNN), enabling the capture of distinctive fire acoustic signatures (e.g., crackling, roaring) that are minimally impacted by… More >

Fahd N. Al-Wesabi^1,*, Hamad Almansour², Huda G. Iskandar^3,4, Ishfaq Yaseen⁵

CMC-Computers, Materials & Continua, Vol.85, No.3, pp. 5765-5787, 2025, DOI:10.32604/cmc.2025.067571 - 23 October 2025

Abstract Earlier notification and fire detection methods provide safety information and fire prevention to blind and visually impaired (BVI) individuals in a limited timeframe in the event of emergencies, particularly in enclosed areas. Fire detection becomes crucial as it directly impacts human safety and the environment. While modern technology requires precise techniques for early detection to prevent damage and loss, few research has focused on artificial intelligence (AI)-based early fire alert systems for BVI individuals in indoor settings. To prevent such fire incidents, it is crucial to identify fires accurately and promptly, and alert BVI personnel… More >

Christine Dewi^1,2, Melati Viaeritas Vitrieco Santoso¹, Hanna Prillysca Chernovita³, Evangs Mailoa¹, Stephen Abednego Philemon¹, Abbott Po Shun Chen^4,*

CMC-Computers, Materials & Continua, Vol.84, No.3, pp. 5361-5379, 2025, DOI:10.32604/cmc.2025.067381 - 30 July 2025

Abstract Early detection of Forest and Land Fires (FLF) is essential to prevent the rapid spread of fire as well as minimize environmental damage. However, accurate detection under real-world conditions, such as low light, haze, and complex backgrounds, remains a challenge for computer vision systems. This study evaluates the impact of three image enhancement techniques—Histogram Equalization (HE), Contrast Limited Adaptive Histogram Equalization (CLAHE), and a hybrid method called DBST-LCM CLAHE—on the performance of the YOLOv11 object detection model in identifying fires and smoke. The D-Fire dataset, consisting of 21,527 annotated images captured under diverse environmental scenarios… More >

Van-Ha Hoang¹, Jong Weon Lee¹, Chun-Su Park^2,*

CMC-Computers, Materials & Continua, Vol.83, No.3, pp. 4097-4116, 2025, DOI:10.32604/cmc.2025.063468 - 19 May 2025

Abstract Fire can cause significant damage to the environment, economy, and human lives. If fire can be detected early, the damage can be minimized. Advances in technology, particularly in computer vision powered by deep learning, have enabled automated fire detection in images and videos. Several deep learning models have been developed for object detection, including applications in fire and smoke detection. This study focuses on optimizing the training hyperparameters of YOLOv8 and YOLOv10 models using Bayesian Tuning (BT). Experimental results on the large-scale D-Fire dataset demonstrate that this approach enhances detection performance. Specifically, the proposed approach… More >

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

CMC-Computers, Materials & Continua, Vol.82, No.3, pp. 5343-5361, 2025, DOI:10.32604/cmc.2025.061466 - 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… More >

Honglin Wang¹, Yangyang Zhang^2,*, Cheng Zhu³

CMC-Computers, Materials & Continua, Vol.82, No.2, pp. 3399-3417, 2025, DOI:10.32604/cmc.2024.058932 - 17 February 2025

Abstract Forest fires pose a serious threat to ecological balance, air quality, and the safety of both humans and wildlife. This paper presents an improved model based on You Only Look Once version 5 (YOLOv5), named YOLO Lightweight Fire Detector (YOLO-LFD), to address the limitations of traditional sensor-based fire detection methods in terms of real-time performance and accuracy. The proposed model is designed to enhance inference speed while maintaining high detection accuracy on resource-constrained devices such as drones and embedded systems. Firstly, we introduce Depthwise Separable Convolutions (DSConv) to reduce the complexity of the feature extraction… More >

Trong Thua Huynh^*, Hoang Thanh Nguyen, Du Thang Phu

CMC-Computers, Materials & Continua, Vol.81, No.2, pp. 2281-2298, 2024, DOI:10.32604/cmc.2024.057954 - 18 November 2024

Abstract In recent years, early detection and warning of fires have posed a significant challenge to environmental protection and human safety. Deep learning models such as Faster R-CNN (Faster Region based Convolutional Neural Network), YOLO (You Only Look Once), and their variants have demonstrated superiority in quickly detecting objects from images and videos, creating new opportunities to enhance automatic and efficient fire detection. The YOLO model, especially newer versions like YOLOv10, stands out for its fast processing capability, making it suitable for low-latency applications. However, when applied to real-world datasets, the accuracy of fire prediction is… More >

Abdul Rehman, Dongsun Kim^*, Anand Paul

CMC-Computers, Materials & Continua, Vol.77, No.2, pp. 2289-2307, 2023, DOI:10.32604/cmc.2023.036435 - 29 November 2023

Abstract Disasters such as conflagration, toxic smoke, harmful gas or chemical leakage, and many other catastrophes in the industrial environment caused by hazardous distance from the peril are frequent. The calamities are causing massive fiscal and human life casualties. However, Wireless Sensors Network-based adroit monitoring and early warning of these dangerous incidents will hamper fiscal and social fiasco. The authors have proposed an early fire detection system uses machine and/or deep learning algorithms. The article presents an Intelligent Industrial Monitoring System (IIMS) and introduces an Industrial Smart Social Agent (ISSA) in the Industrial SIoT (ISIoT) paradigm.… More >

Mesfer Al Duhayyim¹, Majdy M. Eltahir², Ola Abdelgney Omer Ali³, Amani Abdulrahman Albraikan⁴, Fahd N. Al-Wesabi², Anwer Mustafa Hilal^5,*, Manar Ahmed Hamza⁵, Mohammed Rizwanullah⁵

CMC-Computers, Materials & Continua, Vol.77, No.1, pp. 1355-1371, 2023, DOI:10.32604/cmc.2023.024198 - 31 October 2023

Abstract Earth resource and environmental monitoring are essential areas that can be used to investigate the environmental conditions and natural resources supporting sustainable policy development, regulatory measures, and their implementation elevating the environment. Large-scale forest fire is considered a major harmful hazard that affects climate change and life over the globe. Therefore, the early identification of forest fires using automated tools is essential to avoid the spread of fire to a large extent. Therefore, this paper focuses on the design of automated forest fire detection using a fusion-based deep learning (AFFD-FDL) model for environmental monitoring. The… More >

Yuchen Zhao, Shulei Wu^*, Yaoru Wang, Huandong Chen^*, Xianyao Zhang, Hongwei Zhao

CMC-Computers, Materials & Continua, Vol.75, No.3, pp. 5639-5657, 2023, DOI:10.32604/cmc.2023.037829 - 29 April 2023

Abstract Due to the rapid growth and spread of fire, it poses a major threat to human life and property. Timely use of fire detection technology can reduce disaster losses. The traditional threshold segmentation method is unstable, and the flame recognition methods of deep learning require a large amount of labeled data for training. In order to solve these problems, this paper proposes a new method combining You Only Look Once version 5 (YOLOv5) network model and improved flame segmentation algorithm. On the basis of the traditional color space threshold segmentation method, the original segmentation threshold… More >