GEMN: A Novel Forest Fire Detection Network

Forest fires have had a significant impact on global ecosys tems and human societies, necessitating the development of efficient and accurate early smoke detection technologies to combat fires. However, current smoke detection technologies face multiple challenges in real time applications, including issues such as large parameter sizes, high computational complexity, and low detection accuracy in complex sce narios. Therefore, based on YOLOv10, we propose a lightweight, high precision, real-time smoke detection network called GEMN. Firstly, to reduce the extraction of redundant features, we innovatively designed a GGCAattention mechanism. This mechanism significantly enhances the comprehensiveness of the model’s feature extraction by strengthening the representation of important features. Secondly, to lower the compu tational complexity and parameter count of the model, we introduced a lightweight detection head named EISDH. Thirdly, we incorporated the MPDIoU function. This not only enhances the model’s robustness but also simplifies the process of extracting unnecessary features from forest fire targets, further reducing the parameter count in the network model. GEMN demonstrates exceptional performance across three test ing benchmark datasets. Notably, on the FFES dataset, compared to the baseline model, our GEMN model achieves a remarkable 0.991 mAP improvement of 2.5 , reaching an accuracy of 96.8 , an increase of 3.7 . Meanwhile, it compresses the parameters to 4.0MB and short ens the inference time to 1.0 milliseconds, showing an approximately 30 improvement over the original model.