# Precision-Boosted Forest Fire Target Detection via Enhanced YOLOv8 Model
###### Abstract  
Forest fires present a significant challenge to ecosystems, particularly due to factors like tree cover that complicate fire detection tasks. While fire detection technologies, like YOLO, are widely used in forest protection, capturing diverse and complex flame features remains challenging. Therefore, we propose an enhanced YOLOv8 multiscale forest fire detection method. This involves adjusting the network structure and integrating Deformable Convolution and SCConv modules to better adapt to forest fire complexities. Additionally, we introduce the Coordinate Attention mechanism in the Detection module to more effectively capture feature information and enhance model accuracy. We adopt the W1 oU v3 loss function and implement a dynamically non-monotonic mechanism to optimize gradient allocation strategies. Our experimental results demonstrate that our model achieves a mAP of 90.02%, approximately 5.9% higher than the baseline YOLOv8 network. This method significantly improves forest fire detection accuracy, reduces False Positive rates, and demonstrates excellent applicability in real forest fire scenarios. forest fire detection; YOLOv8; SCConv; deformable convolution; CoordAtt mechanism; W1 oU v3 202420241  
clear images [8], it falls short in achieving real-time detection, especially when affected by weather and cloud cover. In comparison, cameras as sensors possess superior real-time performance, particularly when installed on drones, enabling effective detection in more remote forest areas. Researchers have proposed a series of highly effective fire detection systems by integrating color characteristics, color-space models, and motion features using various methodologies. [PERSON], [PERSON] et al. [9] have successfully created a holistic application for forest fire detection and monitoring. This application harnesses flame chromaticity and the Lab color model, incorporating techniques for enhancing image quality and processing. [PERSON] et al. [10] utilized background subtraction and a color-segmentation model, employing support vector machines to classify areas as either genuine fires or non-fires. These studies emphasize the holistic application of flame chromaticity, color space, and motion features, providing diverse and refined approaches to forest fire detection. [PERSON] et al. [11] introduced an effective approach for addressing various fire detection scenarios. This method involves the extraction of texture features, both static and dynamic, from regions identified as potential fire areas, utilizing the YvbCr color model. Additionally, [PERSON] et al. [12] focused on flame extraction, facilitating the precise localization of potential fire locations through the integration of multi-color detection and motion points in RGB, HIS, and YUV color spaces. Recently, there has been a trend towards combining prevalent algorithms in machine learning or deep learning with remote sensing techniques and fire detection systems [13]. [PERSON] et al. [14] proposed a Cat Swarm Fractional Calculus Optimization algorithm for deep learning, combining the optimal features of Cat Swarm Optimization with fractional calculus to achieve superior training results. [PERSON] et al. [15] utilized the Faster R-CNN architecture for fire detection, proposing an approach that integrates both deep learning networks and multidimensional texture analysis to identify potential fire areas through image analysis. [PERSON] et al. [16] utilized the AFFD-ASODTL model, incorporating Atom Search Optimizer and deep transfer learning for automated forest fire detection, thereby reducing response time and minimizing wildfire destruction. [PERSON] et al. [17] proposed TCA-YOLO, an efficient and accurate global forest fire detection model based on YOLOv5 and a Transformer encoder. The model includes a coordinated attention mechanism and adaptive spatial feature-fusion technology, reducing manual labeling efforts through semi-supervised learning. TCA-YOLO demonstrates excellent performance across various scenarios. [PERSON] et al.