Small target smoke detection algorithm based on improved YOLOv5

doi:10.16265/j.cnki.issn1003-3033.2023.11.0854

Abstract

Abstract:

In order to solve the problem of low accuracy of smoke detection for small target in fire, a smoke detection algorithm based on improved YOLOv5 was proposed. Firstly, FFA module was introduced into the backbone network, so that the model focused on the extraction of smoke feature information of small target. Secondly, the convolutional layer module was replaced by MPDH module to improve the detection part of the prediction head layer in the YOLOv5 algorithm, which was used to improve the positioning accuracy of small target smoke. Finally, the experimental results and analysis were carried out on the proprietary data set. The results show that the improved YOLOv5 small target smoke detection algorithm achieves 85.4% target detection accuracy, and the accuracy and recall rate are improved by 3.2% and 6.3% respectively compared with the original algorithm.

Key words: small target smoke, smoke detection algorithm, YOLOv5, feature fusion attention (FFA), multi-scale pyramid decoupling head (MPDH)

ZHANG Jun, YIN Liu, GONG Xinfei, XU Hehua. Small target smoke detection algorithm based on improved YOLOv5[J]. China Safety Science Journal, 2023, 33(11): 75-81.

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算法名称		核心思想	优势	不足
Faster R-CNN		引入区域生成网络生成候选目标区域,并通过共享特征提取和多任务损失函数实现目标的分类和位置回归	提出区域生成网络,检测精度提升	检测速度慢,小目标检测效果不佳
SSD		将目标检测任务转化为一个回归问题和一个分类问题,采用不同尺度和长宽比在不同尺度的特征图中进行目标检测	采用多尺度特征图进行检测,检测速度快	训练难度高,定位精度有限,小目标检测率低
算法名称		核心思想	优势	不足
YOLO	v1	将目标检测任务转化为一个回归问题,并通过单个神经网络同时进行目标的定位和分类	端到端训练,复杂性降低,检测速度快	密集目标检测率低,小目标检测效果差
	v2		引入多尺度训练,泛化能力强,检测效果好	使用预训练,难以迁移,训练速度慢
	v3		首次采用跨阶段局部网络结构,检测准确性提升	结构复杂度高,召回率差,小目标检测不精准
	v4		引入路径聚合网络,检测精度提高	复杂场景下误检率高,小目标的特征信息不易提取
	v5		不同场景适应能力强,检测速度快	小目标检测精度不准确,相互靠近的目标检测效果差

算法名称	mAP@.5/%	P/%	R/%	FPS
Faster R-CNN	85.2	86.4	79.5	46
SSD	78.5	77.2	72	238
YOLOv5	81.1	83.7	74.1	324
MAPDH	85.4	85.8	80.4	289

改进名称	FFA 模块	MPDH 模块	P/%	R/%	mAP@.5: .95/%
YOLOv5	×	×	83.7	74.1	38.6
改进1	√	×	82.9	79.4	40.6
改进2	×	√	84.5	77.2	40.8
MAPDH	√	√	85.8	80.4	43.2