改进的YOLOv5烟雾检测模型. (Chinese)

Journal of Computer Engineering & Applications; Apr2023, Vol. 59 Issue 7, p214-221, 8p

SMOKE, ALGORITHMS, FORECASTING, CLASSIFICATION, TRACKING radar

Aiming at the problems of complex smoke occurrence scene and difficult smoke detection of small targets, an improved YOLOv5 smoke detection model is proposed. Firstly, in order to increase the detection accuracy of the target smoke, the feature fusion process is modified by combining the weighted bidirectional feature Pyramid network（BiFPN） structure, and the mixed attention mechanism is added to the channel and spatial dimensions to reassign the weight of the fused feature map. In enhanced characteristics of the smoke target inhibition has nothing to do with regional characteristics at the same time, the smoke has higher robustness characteristics of expression. Secondly, α-CIOU is used to replace G-IOU as the prediction box regression loss to improve the prediction accuracy of the prediction box. The classification loss is removed to reduce the complexity of the model. Experimental results show that the improved YOLOv5 smoke detection model has higher detection accuracy than the YOLOv5 model, with an accuracy of 99.35% and a recall rate of 99.18% and a detection speed of 46 frame/s. The proposed algorithm can effectively extract the overall characteristics of smoke, which is more suitable for smoke detection tasks in complex scenes and small targets. [ABSTRACT FROM AUTHOR]

针对烟雾发生场景复杂, 小目标烟雾检测困难的问题, 提出一种改进的YOLOv5烟雾检测模型.为了增加模型对目标烟雾的检测精度, 结合加权双向特征金字塔网络（BiFPN）结构对特征融合过程进行修改, 并在通道和空间维度上加入混合注意力机制对融合特征图的权重进行重新赋值, 在增强烟雾目标特征的同时抑制无关区域特征, 使烟雾特征表达具有更高的鲁棒性; 使用α-CIOU替换G-IOU作为预测框回归损失, 提升预测框的预测精度; 剔除分类损失以降低模型的复杂度.实验结果表明, 改进后的YOLOv5烟雾检测模型相比于YOLOv5模型检测精度更高, 其准确率达到99.35%, 召回率达到99.18%, 并且检测速度可达46?frame/s, 该算法能有效提取烟雾的整体特征, 对于复杂场景下的烟雾以及小目标烟雾检测任务更为适用. [ABSTRACT FROM AUTHOR]

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