【目的】森林火灾的频发严重破坏了自然环境并威胁到人身安全,因此及时检测出火灾源头是灭火的关键。森林中树木密度大、底层枯落物堆积和树冠层繁茂等自然环境对森林火灾的检测造成巨大的干扰。为了更准确地检测森林火灾,提出一种基于SBP-YOLOv7的森林火灾检测方法。【方法】首先,通过引入注意力机制增强的下采样模块 (BRA-MP),在下采样过程中增强特征选择能力,有效增强特征表示能力和语义关联性,从而提升模型对小目标的检测能力。其次,在模型的主干部分提出一种扩展部分卷积的高效层聚合模块(EP-ELAN),通过减少冗余计算以降低模型参数量。最后,采用Slim-neck颈部模块进行特征融合,在保证精度的同时降低模型的计算成本。【结果】在林火目标数据集上进行的对比验证结果显示,SBP-YOLOv7模型的平均精度(AP)达到87.0%,比原YOLOv7模型的AP增加2.3%。另外,SBP-YOLOv7模型参数量较YOLOv7降低22.77%,计算量降低17.13%。【结论】相比于传统YOLOv7算法,提出的SBP-YOLOv7模型拥有更高的精度和更少的参数,能够准确快速地检测出森林火灾。

【Objective】Forest fires pose a significant threat to the natural environment and human safety, thus timely and accurate detection of fire sources is demanding. However, the complex forest environment characterized by high tree density, ground litter accumulation, and dense canopies creates substantial challenges for effective fire detection. To address these issues, this study proposes a novel forest fire detection method, SBP-YOLOv7.【Method】The proposed method incorporated three key innovations. First, an attention mechanism-enhanced downsampling module (BRA-MP) was introduced to improve feature recognition during downsampling, enhancing the model’s ability to detect small targets by boosting feature representation and semantic relevance. Second, the extended partial convolution efficient layer aggregation module (EP-ELAN) was integrated into the model’s backbone, effectively reducing redundant computations and minimizing model parameters. Finally, a Slim-neck neck module was employed for feature fusion, ensuring high accuracy while lowering computational costs. 【Result】Comparative evaluations on a forest fire dataset demonstrate that the SBP-YOLOv7 model achieves an AP score of 87.0%, representing 2.3% improvement over the original YOLOv7. Additionally, the model reduces parameter count by 22.77% and computational cost by 17.13%.【Conclusion】Compared with the traditional YOLOv7 algorithm, the proposed SBP-YOLOv7 model offers superior accuracy and efficiency, enabling faster and more precise detection of forest fires even in challenging environments.