Compare with other lightweight networks.

Smoke and fire detection technology is a key technology for automatically realizing forest monitoring and forest fire warning. One of the most popular algorithms for object detection tasks is YOLOv5. However, it suffers from some challenges, such as high computational load and limited detection performance. This paper proposes a high-performance lightweight network model for detecting forest smoke and fire based on YOLOv5 to overcome these problems. C3Ghost and Ghost modules are introduced into the Backbone and Neck network to achieve the purpose of reducing network parameters and improving the feature’s expressing performance. Coordinate Attention (CA) module is introduced into the Backbone network to highlight the object’s important information about smoke and fire and to suppress irrelevant background information. In Neck network part, in order to distinguish the importance of different features in feature fusing process, the weight parameter of feature fusion is added which is based on PAN (path aggregation network) structure, which is named PAN-weight. Multiple sets of controlled experiments were conducted to confirm the proposed method’s performance. Compared with YOLOv5s, the proposed method reduced the model size and FLOPs by 44.75% and 47.46% respectively, while increased precision and mAP(mean average precision)@0.5 by 2.53% and 1.16% respectively. The experimental results demonstrated the usefulness and superiority of the proposed method. The core code and dataset required for the experiment are saved in this article at https://github.com/vinchole/zzzccc.git.

烟雾与火情检测技术是实现森林自动监测与森林火灾预警的核心技术。目标检测任务中应用最为广泛的算法之一当属YOLOv5，但该算法仍存在计算负载较高、检测性能受限等挑战。为此，本文提出一种基于YOLOv5的高性能轻量化网络模型，用于森林烟雾与火情检测以解决上述问题。本文将C3Ghost模块与Ghost模块引入主干网络（Backbone）与颈部网络（Neck），以降低网络参数量、提升特征表达性能。此外，本文将坐标注意力（Coordinate Attention, CA）模块引入主干网络，用以突出烟雾与火情目标的关键信息，抑制无关背景信息。在颈部网络部分，为区分特征融合过程中不同特征的重要程度，本文基于路径聚合网络（Path Aggregation Network, PAN）结构引入特征融合权重参数，将改进后的结构命名为PAN-weight。通过多组对照实验验证了所提方法的性能：与基准模型YOLOv5s相比，本文所提方法将模型尺寸与浮点运算量（FLOPs）分别降低了44.75%与47.46%，同时将精确率与平均精度均值（mean average precision, mAP）@0.5分别提升了2.53%与1.16%。实验结果证明了所提方法的有效性与优越性。本文实验所需的核心代码与数据集已开源至：https://github.com/vinchole/zzzccc.git。