氨气泄漏混洗自注意力轻量化红外检测 -

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氨气泄漏混洗自注意力轻量化红外检测

doi:10.37188/CO.2022-0127

昆明理工大学机电工程学院, 云南昆明 650500

基金项目:国家自然科学基金（No. 62061022，No. 62171206，No. 61761024）

详细信息

作者简介:
张印辉（1977—），男，河北故城人，博士，教授，博士生导师，2010年于昆明理工大学获得博士学位，现为昆明理工大学机电工程学院教授，主要研究方向为图像处理、机器视觉。E-mail：zhangyinhui@kust.edu.cn
何自芬（1976—），女，山西阳泉人，博士，副教授，2013年于昆明理工大学获得博士学位。现为昆明理工大学机电工程学院副教授，主要研究方向为图像处理、计算机视觉。E-mail：zyhhzf1998@163.com

中图分类号:TP391
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出版历程
- 收稿日期:2022-06-14
- 修回日期:2022-07-07
- 网络出版日期:2022-09-28
- 刊出日期:2023-04-11

Lightweight infrared detection of ammonia leakage using shuffle and self-attention

Faculty of Mechanical and Electrical Engineering, Kunming University of Science and Technology, Kunming 650500, China

Funds:Supported by National Natural Science Foundation of China (No. 62061022, No. 62171206, No. 61761024)

More Information

Corresponding author:zyhhzf1998@163.com

摘要

摘要:
氨气是重要的基础工业原材料，实现其非接触探测对于及时发现氨气泄漏，避免重大安全事故发生具有重要意义。针对常规氨气泄漏检测装置需等到氨气扩散到一定范围并与传感器接触时才能响应的不足，提出一种混洗自注意力网络(SSANet)模型实现氨气泄漏红外非接触检测。因红外热像仪获取的氨气泄漏图像含噪高、对比度低，故通过非局部均值去噪、限制对比度的自适应直方图均衡化预处理建立氨气泄漏红外检测数据集。SSANet模型在YOLOv5s基础上通过K-means算法聚类分析出适用于氨气泄漏红外检测的候选框以预置模型参数；采用轻量级ShuffleNetv2网络，将其Shuffle Block中的3×3的深度可分离卷积核替换为5×5，采用含有新卷积模块的SK5 Block对特征提取网络进行重构，使模型大小、计算量和参数量实现轻量化的同时提高检测精度；采用Transformer模块代替原网络瓶颈模块中的C3模块实现泄漏区域多头注意力自底向上融合，实现检测精度的再次提升。实验结果表明，SSANet模型较YOLOv5s基础模型大小和参数量分别减少76.40%、78.30%，降为3.40 M、1.53 M；单张图像平均检测速度提升1.10%，达到3.20 ms；平均检测精度提升3.50%，达到96.30%。本文为开发氨气泄漏非接触探测装置以保障涉氨企业的安全生产和稳定运行提供了一种有效的检测算法。
- 氨气泄漏检测/
- 红外图像/
- 聚类分析/
- 轻量化结构/
- Transformer模块
Abstract:
Ammonia gas is an important basic industrial raw material, and realizing its non-contact detection is of great significance for the timely detection of ammonia gas leaks to avoid major safety incidents. Aiming at the shortcoming of conventional ammonia leak detection devices that can only respond when ammonia diffuses to a certain range and makes contact with a sensor, a Shuffling Self-Attention Network (SSANet) model is proposed to realize the infrared non-contact detection of ammonia leaks. Due to the high noise and low contrast of ammonia leakage images obtained by infrared cameras, an infrared detection dataset of ammonia leakage was established through non-local mean denoising and contrast-limited adaptive histogram equalization preprocessing. On the basis of YOLOv5s, the SSANet model uses the K-means algorithm to cluster and analyze the candidate frame suitable for the infrared detection of ammonia gas leakage to preset the model’s parameters. Using the lightweight ShuffleNetv2 network, the depth of 3×3 in the Shuffle Block can be adjusted. The separate convolution kernel is replaced with a 5×5 depth, and the feature extraction network is reconstructed with an SK5 Block containing a new convolution module, which makes the model size, calculation and parameters non-intensive while improving the detection accuracy. The Transformer module is used instead of its original version. The C3 module in the network bottleneck module is replaced by Transformer module to realize the bottom-up fusion of multi-head attention in the leake area, and further improves the detection accuracy. The experimental results show that the size and parameter requirements of the SSANet model are reduced by 76.40% and 78.30%, respectively, to 3.40 M and 1.53 M compared with the basic model of YOLOv5s; the average detection speed of a single image is increased by 1.10% to 3.20 ms; and the average detection accuracy is increased by 3.50% , reaching 96.30%. We provide an effective detection algorithm for the development of a non-contact detection device for ammonia leakage to ensure the safe production and stable operation of ammonia-related enterprises.
- ammonia leak detection/
- infrared image/
- cluster analysis/
- lightweight structure/
- transformer module

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图 1SSANet 模型总体架构

Figure 1.The overall architecture of the SSANet model

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图 2红外氨气泄漏真实框变化图

Figure 2.Change diagram of a real frame of infrared ammonia leakage

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图 3氨气泄漏红外检测数据集候选框高宽比可视化结果

Figure 3.Visualization results of the height/width ratio of the anchor in ammonia leak infrared detection data

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图 4通道混洗实现方式

Figure 4.Implementation of channel shuffling

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图 5SK5 Block模块结构

Figure 5.SK5 Block module structure

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图 6Transformer模块结构图

Figure 6.Structure diagram of Transformer block

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图 7Transformer编码层结构图

Figure 7.Transformer encode structure diagram

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图 8不同方法处理后的增强效果对比图

Figure 8.Comparison of enhancement effects by different methods

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图 9SSANet模型最终检测结果

Figure 9.The final test results of the SSANet network model

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表 1聚类前后3个检测层初始候选框尺寸情况

Table 1.Initial candidate frame sizes of the three detection layers before and after clustering

检测层	聚类前	聚类后
检测层1	（10,13）、（16,30）、（33,23）	（11,10）、（29,12）、（34,29）
检测层2	（30,61）、（62,45）、（59,119）	（52,61）、（62,18）、（64,38）
检测层3	（116,90）、（156,198）、（373,326）	（91,38）、（115,22）、（201,45）

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表 2超参数配置

Table 2.Hyperparameter configuration

超参数名称	超参数值
批大小	16
初始学习率	0.01
迭代次数	400
动量	0.937
学习率衰减策略	余弦退火策略
权重衰减	0.0005

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表 3图像预处理的定量评价指标

Table 3.Objective evaluation indicators of image preprocessing algorithms

图像	PSNR/dB	AG	IE
原图像	23.40	1.90	7.06
预处理图像	23.40	2.17	7.53

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表 4图像预处理前后网络性能对比

Table 4.Comparison of network performances before and after image preprocessing

模型	Params/M	Model size/M	Speed/ms	mAP/%
YOLOv5s	7.05	14.40	3.60	92.80
Prep-YOLOv5s	7.05	14.40	3.60	93.80

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表 5聚类前后网络性能对比

Table 5.Comparison of network performance before and after clustering

模型	Params/M	Model size/M	Speed/ms	mAP/%
YOLOv5s	7.05	14.40	3.60	92.80
Kms-YOLOv5s	7.05	14.40	3.60	93.70
Kms-Prep-YOLOv5s	7.05	14.40	3.60	94.30

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表 6不同特征提取网络评估指标对比

Table 6.Comparison of evaluation indicators for different backbone networks

模型	GFLOPs	Model size/M	Params /M	Speed /ms	mAP /%
GhostNet-YOLOv5s	10.60	10.50	5.08	3.00	93.90
MobileNetv3-YOLOv5s	6.30	7.40	3.54	2.80	93.50
ShuffleNetv2-YOLOv5s	4.60	3.40	1.53	2.70	93.80
SK5-YOLOv5s	4.80	3.40	1.57	2.70	94.40

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表 7不同BottleNeck结构网络性能对比

Table 7.Comparison of network performance of different BottleNeck structures

模型	Model size/M	Params /M	Speed /ms	mAP /%
SK5-YOLOv5s	3.40	1.57	2.70	94.40
SK5-YOLOv5s-CSPBottleNeck	3.40	1.54	2.70	93.70
SK5-YOLOv5s-GhostBottleNeck	3.30	1.50	2.60	94.20
SK5-YOLOv5s-CbamBottleNeck	3.20	1.47	2.50	92.90
SSANet	3.40	1.53	3.20	96.30

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表 8不同模型精度对比

Table 8.Accuracy comparison of different models

Model	GFLOPs	Params/M	Model size/M	Speed/ms	mAP/%
YOLOv3	154.7	61.50	123.40	11.70	92.70
YOLOv3-tiny	12.90	8.70	17.40	3.40	37.40
YOLOv5s	16.30	7.05	14.40	3.60	92.80
YOLOx	26.64	8.94	71.90	8.43	89.78
SSANet	4.60	1.53	3.40	3.20	96.30

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