# A Lightweight Fire Detection Algorithm Based on the Improved YOLOv8 Model
## 4 Conclusions  
This study confronts the challenges of existing fire detection systems, which often lack robustness in complex environments and have difficulties in accurately detecting fires at an early stage. To improve upon the YOLOv8 baseline model, we propose the GCM-YOLO lightweight fire detection algorithm. GCM-YOLO enhances the YOLOv8n backbone by integrating GhostNet, introducing the CARAFEs upsampling module, and incorporating the MLCA attention mechanism to enhance model precision. GhostNet is a lightweight neural network architecture. It reduces computational cost by employing a small number of actual convolutional kernels to generate the feature maps and then utilizes economical linear operations to generate additional feature maps, which demonstrates significant superiority in terms of model lightweight when compared with FasterNet and HGNetv2 networks. The CARAFEs upsampling module generates reconfigured weights by exploiting the content information within the input feature maps, allowing the upsampled feature maps to retain more detailed information, thereby improving the detection accuracy of the model. The MLCA attention mechanism integrates channel attention, spatial attention, and local and global information and is capable of distributing attention resources more effectively, enhancing the weights of crucial features, and suppressing irrelevant or noisy features. Compared with SEAttention, CBAM, and SimAM attention mechanisms, it achieves the optimum performance in terms of accuracy. Experimental results suggest that GCM-YOLO attains 82.9% of the [EMAIL_ADDRESS], representing a 1.2% increase in average accuracy compared to YOLOv8n. Additionally, it reduces the model parameters and FLOP by 38.3% and 34.6%, respectively. With the enhanced model efficiency and accuracy, GCM-YOLO proves highly appropriate for deployment in resource-constrained environments and has considerable practical applications in diverse scenarios. This study contributes novel knowledge to the realm of assertive decision-making in the supervision of security and fire protection systems. It introduces innovative feature extraction methods and attention mechanisms that exhibit practical feasibility in resource  
Figure 8: Visual comparison of detection performance. (**a**) Road Background; (**b**) Dim Indoor Background; (**c**) Complex Outdoor Background; (**d**) Artificial Light Interference. constrained environments. Nevertheless, it is undeniable that although the model performs well in resource-constrained environments, real-time performance constitutes a key metric in practical applications. The performance of the model's inference speed on different hardware platforms needs further assessment and optimization. Future research should focus on investigating the reliability and security of the developed algorithms and the modernized security system as a whole. Comprehensive reliability tests and security evaluations will ensure the robustness of GCM-YOLO in real-world applications. Conceptualization, S.M. and W.L. ; methodology, S.M. and W.L. ; software, W.L. and L.W. ; validation, W.L. and G.Z. ; formal analysis, W.L. and L.W. ; investigation, W.L. ; resources, S.M. ; data curation, W.L. ; writing--original draft preparation, W.L. ; writing--review and editing, S.M. ; visualization, W.L. ; supervision, S.M. ; project administration, G.Z. ; funding acquisition, S.M. All authors have read and agreed to the published version of the manuscript. This research was funded by the National Natural Science Foundation of China, Project No. 62103309. Not applicable. The full dataset cannot be publicly disclosed due to privacy concerns. However, we can provide a subset of the data containing the main features. The data, models, and codes that support the findings of this study can be accessed by contacting the corresponding author upon reasonable request.