The existing YOLOv5‐based framework has achieved great success in the field of target detection. However, in forest fire detection tasks, there are few high‐quality forest fire images available, and the performance of the YOLO model has suffered a serious decline in detecting small‐scale forest fires. Making full use of context information can effectively improve the performance of small target detection. To this end, this paper proposes a new graph‐embedded YOLOv5 forest fire detection framework, which can improve the performance of small‐scale forest fire detection using different scales of context information. To mine local context information, we design a spatial graph convolution operation based on the message passing neural network (MPNN) mechanism. To utilize global context information, we introduce a multi‐head self‐attention (MSA) module before each YOLO head. The experimental results on FLAME and our self‐built fire dataset show that our proposed model improves the accuracy of small‐scale forest fire detection. The proposed model achieves high performance in real‐time performance by fully utilizing the advantages of the YOLOv5 framework.

中文翻译：

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用于森林火灾检测的有效图嵌入YOLOv5模型

现有的基于YOLOv5的框架在目标检测领域取得了巨大的成功。然而，在森林火灾检测任务中，可用的高质量森林火灾图像很少，YOLO模型在检测小规模森林火灾时性能严重下降。充分利用上下文信息可以有效提高小目标检测的性能。为此，本文提出了一种新的图嵌入YOLOv5森林火灾检测框架，可以利用不同尺度的上下文信息提高小规模森林火灾检测的性能。为了挖掘局部上下文信息，我们设计了基于消息传递神经网络（MPNN）机制的空间图卷积运算。为了利用全局上下文信息，我们在每个 YOLO head 之前引入了一个多头自注意力（MSA）模块。 FLAME和我们自建的火灾数据集上的实验结果表明，我们提出的模型提高了小规模森林火灾检测的准确性。所提出的模型充分利用YOLOv5框架的优势，在实时性能上实现了高性能。