## 2 Related Work  
State-of-the-artwork detects the forest fires utilizing a wide-area sensor network [12; 13]; however, the deployment of such a network in a dense forest is expensive and not practical. Alternatively, hyperspectral data may also be utilized in this context for spotting a forest fire in a larger region [14; 15; 16]. However, it has a low temporal and spatial resolution, thus narrowing down the surveillance area [17; 18]. The most often employed method is to acquire visible or infrared images via exploration flights with planes or low-cost drones [19; 20]. In most cases, detecting the flame and alarming the firefighters with real fires is a crucial task [21], thus, enabling locating the actual fires and moving the firefighter to the desired location for extinguishing and preventing further fire spread. Several works classify the fire and non-fire region from the image dataset [10; 22]. It is broadly categorized into two groups: fire detection (flame detection) [23; 24; 25] and early fire detection (smoke detection) [26; 27; 28]. The former detection is challenging, as the smoke persists for hours even after the fire stops, thus making real fire detection more complex, with acquired images or on-site. State-of-the-art work performs wildfire detection with two strategies: target localization (decision-based techniques) and segmentation [29; 30]. Moreover, there are recent studies that had proposed frameworks based on either traditional classification and image processing methods [31; 32] or deep learning techniques [33; 34]. The latter gained research interest in the past decade for real-time applications that perform faster and more efficient learning on various large image datasets. In recent work, fire segmentation is performed efficiently, adopting a segmentation approach based on traditional methods [29]. Thus, extracting the essential information from near infrared (NIR) and visible images for accurately segmenting the fire images. Furthermore, low and high-frequency components are extracted from visible and IR images of the Corsican dataset [10] using a contourlet-based decomposition. Subsequently, the low-frequency information of visible and IR images is fused utilizing a pulse-coupled neural network (PCNN). While the high-frequency components are fused using the Local log Gabor energy-based fusion rule. Lastly, with implementing the fuzzy C-means clustering (FCM) algorithm, segmentation of images is performed and an accuracy of 98.45% is achieved over the Corsican French dataset [10]. A recent study performed fire segmentation adopting a lightweight deep learning architecture, namely squeezed fire binary segmentation network (SFBSNet) [35]. SFBSNet performed the segmentation adopting the traditional encoder-decoder architecture with depth-wise separable convolution layers that induce a richer feature map. The adopted approach attained a mean Intersection over Union (mIoU) of 90% on a Corsican dataset, highlighting certain model limitations. A recent study implemented Deeplabv3 architecture for fire segmentation on a custom augmented dataset with some image processing algorithms [36]. Experiments were performed on datasets adopting three backbones: ResNet-50, ResNet101, and ResNet-105 and reported a mIoU of 70.51% and an accuracy of 98.78% over the test set. To summarize, the existing studies on wildfire detection widely focus on segmentation and detection, and adopt suitable image processing techniques in conjunction with machine learning models; thus, drastically reducing the time required for data processing as the time grows exponentially with the size of the acquired images. It is highly essential to spot the fire pixels and alert the firefighters without any delay to handle the situation more responsively in a real-time fire situation. However, most existing systems are modeled for fire segmentation in offline mode, as real-time segmentation and detection necessitate data preprocessing. Thus, it is challenging to perform fire detection and segmentation as it attracts high computational costs.