# A Lightweight Fire Detection Algorithm Based on the Improved YOLOv8 Model
## 1 Introduction  
Timely detection and subsequent intervention during the incipient stages of a fire are of paramount significance in restricting its propagation and minimizing potential casualties and property damage [1; 2; 3; 4]. Typically, early fire detection utilizes methods such as smoke sensors, including photoelectric and ionization smoke detectors [5; 6]. Nevertheless, these sensors are prone to environmental conditions and demonstrate low monitoring efficiency, restricting their efficacy in detecting delicate smoke or concealed flames in the initial phases of the fire.  
Advancements in traditional image processing techniques have contributed to the enhancement of fire detection accuracy, particularly through the analysis of color, texture, and shape features [7]. For instance, [PERSON] et al. [8] utilized the YCbCr color model to detect fire regions from video image frames. Their study distinguishes flame pixels from other high-intensity pixels using specific parameters in the YCbCr color space. By analyzing video image frames, an effective method for detecting flame regions was proposed. [PERSON] et al. [9] proposed a novel superpixel synthesis algorithm and enhanced existing horizon detection algorithms by employing support vector machines for superpixel classification. However, these methods often struggle with variations in lighting conditions and background interference [10], thereby limiting their effectiveness in complex environments. In recent years, object detection methods have emerged as promising approaches for fire and smoke detection. [PERSON] [11] proposed a deep learning-based \"Double-Step U-Net\" method that combines classification and regression algorithms to predict the damage/severity levelsof sub-regions within affected areas by processing satellite images following a single fire incident. [PERSON] et al. [12] presented an efficacious wildfire and smoke detection model integrating the YOLO architecture with a voting ensemble of convolutional neural network (CNN) architectures. This model adopts an ensemble of multiple CNN architectures and functions in two stages for the classification and detection of fire and smoke. [PERSON] et al. [13] proposed a domain-independent fire detection algorithm grounded on the YOLOv5 framework, integrating linear attention and a Gated Temporal Pool (GTP) to extract the spatial and temporal features of fires. [PERSON] et al. [14] proposed a lightweight fire detection algorithm based on YOLOv5, incorporating Ghost modules and the CA attention mechanism, alongside feature fusion weight parameters derived from the Path Aggregation Network structure, to enhance detection speed.  
Currently, fire detection algorithms often require increasing the number of network layers to enhance accuracy [15], which demands substantial computational resources and time. Moreover, their robustness and real-time performance in complex environments still need improvement. To address the above problems, this paper proposes a lightweight fire detection network GCM-YOLO based on an improved feature extraction network and attention mechanism. Firstly, a phantom network is introduced to achieve the optimization of the backbone network, which reduces the number of parameters and the amount of computation of the model without losing the accuracy of the model; secondly, the CARAFEs upsampling module is added to enhance the feature information extraction capability, so that the model can better adapt to the targets of different scales; finally, the hybrid local channel attention mechanism is added at the neck to improve the accuracy of the model in detecting targets in complex environments.