Forest fires represent a significant menace to both the ecological equilibrium of forests and the safety of human life and property. Upon ignition, fires frequently generate billowing smoke. The prompt identification and management of fire sources and smoke can efficiently avert the occurrence of extensive forest fires, thereby safeguarding both forest resources and human well-being. Although drone patrols have emerged as a primary method for forest-fire prevention, the unique characteristics of forest-fire images captured from high altitudes present challenges. These include remote distances, small fire points, smoke targets with light hues, and complex, ever-changing background environments. Consequently, traditional target-detection networks frequently exhibit diminished accuracy when handling such images. In this study, we introduce a cutting-edge drone-based network designed for the detection of forest fires and smoke, named FSNet. To begin, FSNet employs the YOCO data-augmentation method to enhance image processing, thereby augmenting both local and overall diversity within forest-fire images. Next, building upon the transformer framework, we introduce the EBblock attention module. Within this module, we introduce the notion of “groups”, maximizing the utilization of the interplay between patch tokens and groups to compute the attention map. This approach facilitates the extraction of correlations among patch tokens, between patch tokens and groups, and among groups. This approach enables the comprehensive feature extraction of fire points and smoke within the image, minimizing background interference. Across the four stages of the EBblock, we leverage a feature pyramid to integrate the outputs from each stage, thereby mitigating the loss of small target features. Simultaneously, we introduce a tailored loss function, denoted as $L_{forest}$, specifically designed for FSNet. This ensures the model’s ability to learn effectively and produce high-quality prediction boxes. We assess the performance of the FSNet model across three publicly available forest-fire datasets, utilizing mAP, Recall, and FPS as evaluation metrics. The outcomes reveal that FSNet achieves remarkable results: on the Flame, Corsican, and D-Fire datasets, it attains mAP scores of 97.2%, 87.5%, and 94.3%, respectively, with Recall rates of 93.9%, 87.3%, and 90.8%, respectively, and FPS values of 91.2, 90.7, and 92.6, respectively. Furthermore, extensive comparative and ablation experiments validate the superior performance of the FSNet model.

Naive Bayes (NB) is one of the essential algorithms in data mining. However, it is rarely used in reality because of the attribute independence assumption. Researchers have proposed many improved NB methods to alleviate this assumption. Among these methods, due to its high efficiency and easy implementation, the filter-attribute-weighted NB methods have received great attentions. However, there still exist several challenges, such as the poor representation ability for a single index and the fusion problem of two indexes. To overcome the above challenges, we propose a general framework of an adaptive two-index fusion attribute-weighted NB (ATFNB). Two types of data description category are used to represent the correlation between classes and attributes, the intercorrelation between attributes and attributes, respectively. ATFNB can select any one index from each category. Then, we introduce a regulatory factor $\beta$ to fuse two indexes, which can adaptively adjust the optimal ratio of any two indexes on various datasets. Furthermore, a range query method is proposed to infer the optimal interval of regulatory factor $\beta$. Finally, the weight of each attribute is calculated using the optimal value $\beta$ and is integrated into an NB classifier to improve the accuracy. The experimental results on 50 benchmark datasets and a Flavia dataset show that ATFNB outperforms the basic NB and state-of-the-art filter-weighted NB models. In addition, the ATFNB framework can improve the existing two-index NB model by introducing the adaptive regulatory factor $\beta$. Auxiliary experimental results demonstrate the improved model significantly increases the accuracy compared to the original model without the adaptive regulatory factor $\beta$.