## 1 Introduction  
Disaster management has been the focus of research across various fields, including computer science, health sciences, environmental sciences, and business. Such disasters can be classified as either technological or natural, according to the Federal Emergency Management Agency [1]. Technological disasters include incidents such as hazardous materials, terrorism, and nuclear disasters, while natural disasters encompass events like earthquakes, floods, and forest fires. Regardless of the type of disaster, early detection, preventive measures, and timely notification of relevant departments are crucial [2]. Fire disasters, often caused by system failures or human error, can result in significant human and ecological losses and economic damage [2; 3; 4]. For example, it was reported that in June 2013 in Arizona (USA), 19 firefighters were killed and 100 houses were burned by a wildfire [5]. To cope with this, researchers have presented various methods for fire detection based on environmental and visual sensors [3; 6]. Environmental sensor-based systems that detect close-range fires are feasible for indoor environments and need human intervention [7; 8]. On the other hand, vision-based methods offer numerous advantages compared to different approaches. They can cover a large geographical area, allowing for more comprehensive space monitoring. Furthermore, they can detect fire in the initial stages and provide a rapid response, which is crucial in emergencies. Another advantage of vision-based fire detection methods is their ability to perform effectively under different environmental conditions, making them a robust solution for fire detection [3; 9]. Surveillance-based fire detection can be broadly categorized into machine- and deep-learning-based methods. The machine learning-based methods rely on color-based features or motion, such as YCbCr [10], RGB [11; 12], YUV [13], and HSV [14], for fire detection. These methods use a set of images to match objects, similar to that of fire. These methods are computationally efficient and can be used in real-time systems. Certain researchers have employed statistical color models in combination with background subtraction techniques to identify pixels indicating the presence of fire [15; 16; 17]. However, these methods have a high false alarm rate and are sensitive to illumination changes. To address the difficulties of fire detection, certain researchers have integrated color attributes and motion information to identify a fire's shape [18; 19; 20; 21]. These studies reduce false alarm rates; however, their accuracy is challenging, and they cannot identify long- or short-distance fires. Moreover, manually designed features in these methods are not optimal due to variations in fire shapes, lighting conditions, and fire colors [22]. Thus, the researchers have explored fire detection techniques that utilize deep learning methods. Deep learning provides numerous applications in various fields, such as segmentation, detection, and classification [2; 3; 23; 24]. Over the past few years, using convolutional neural network (CNN)-based techniques for fire detection has gained popularity, proving effective in uncertain and certain environmental surveillance systems. The application of CNN-based techniques has resulted in notable enhancements to the resilience of fire detection systems in various environments and reduced the frequency of false alarms. Researchers have developed different CNN-based architectures for smoke and fire detection. For example, a study [25] used ResNet50 and VGG-based models to evaluate their performance over a customized dataset, while [PERSON] et al. [26] developed a 9-layered architecture for the underlying task. A study by [PERSON] et al. [27] involved adjusting CNN models for improved early detection of fires and integrating a prioritization system for each node within a monitoring environment.