基于改进YOLOv4-tiny的轻量化室内人员目标检测算法

赵凤; 李永恒; 李晶; 刘汉强

doi:10.11999/JEIT220241

基于改进YOLOv4-tiny的轻量化室内人员目标检测算法

doi: 10.11999/JEIT220241

赵凤^1, ,,
李永恒¹,
李晶¹,
刘汉强²

1.
西安邮电大学通信与信息工程学院西安 710121
2.
陕西师范大学计算机科学学院西安 710119

基金项目: 国家自然科学基金(62071379, 62071378, 61901365, 62106196)，陕西省自然科学基础研究计划 (2021JM-461, 2020JM-299)，西安邮电大学西邮新星团队资助项目(xyt2016-01)

详细信息

作者简介:
赵凤：女，教授，研究方向为智能信息处理、模式识别与图像处理

李永恒：男，硕士生，研究方向为深度学习与目标检测

李晶：男，高级工程师，研究方向为智能信息处理

刘汉强：男，副教授，研究方向为模式识别与图像处理

通讯作者:
赵凤　fzhao.xupt@gmail.com

中图分类号: TN911.73
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出版历程
- 收稿日期: 2022-03-08
- 修回日期: 2022-06-28
- 网络出版日期: 2022-07-05
- 刊出日期: 2022-11-14

Lightweight Indoor Personnel Detection Algorithm Based on Improved YOLOv4-tiny

1.
School of Communications and Information Engineering, Xi’an University of Posts and Telecommunications, Xi’an 710121, China
2.
School of Computer Science, Shaanxi Normal University, Xi’an 710119, China

Funds: The National Natural Science Foundation of China (62071379, 62071378, 61901365, 62106196), The Natural Science Basic Research Plan in Shaanxi Province of China (2021JM-461, 2020JM-299), Funded Project of New Star Team of Xi'an University of Posts & Telecommunications (xyt2016-01)

摘要

摘要: 深度学习在室内人员检测领域应用广泛，但是传统的卷积神经网络复杂度大且需要高算力GPU的支持，很难实现在嵌入式设备上的部署。针对上述问题，该文提出一种基于改进YOLOv4-tiny的轻量化室内人员目标检测算法。首先，设计一种改进的Ghost卷积特征提取模块，有效减少了模型的复杂度；同时，该文通过采用带有通道混洗机制的深度可分离卷积进一步减少网络参数；其次，该文构建了一种多尺度空洞卷积模块以获得更多具有判别性的特征信息，并结合改进的空洞空间金字塔池化结构和具有位置信息的注意力机制进行有效的特征融合，在提升准确率的同时提高推理速度。在多个数据集和多种硬件平台上的实验表明，该文算法在精度、速度、模型参数和体积等方面优于原YOLOv4-tiny网络，更适合部署于资源有限的嵌入式设备。
- 室内人员检测 /
- 深度学习 /
- YOLOv4-tiny /
- Ghost卷积
Abstract: Deep learning has been widely applied to the field of indoor personnel detection. However, the traditional convolutional neural networks have a high complexity and require the support of highly computational GPU. It is difficult to accomplish the implementation in the embedded devices. For the above problems, a lightweight network model based on improved YOLOv4-tiny network is proposed for indoor personnel detection. Firstly, an improved Ghost convolution feature extraction module is designed to reduce effectively the model complexity. Simultaneously, to reduce network parameters, a depth-wise separable convolution with channel shuffle mechanism is adopted in this paper. Secondly, a multi-scale dilated convolution module is developed in this paper to obtain more discriminative feature information, which combines the improved dilated space pyramid pooling module and the attention mechanism with location information for effective feature fusion, thereby improving inference accuracy and inference speed, simultaneously. The experiments on multiple datasets and hardware platforms show that the proposed model is superior to the original YOLOv4-tiny network in terms of accuracy, speed, model parameters and volume. Therefore, the proposed model is more suitable for deployment in resource-limited embedded devices.
- Indoor personnel detection /
- Deep learning /
- YOLOv4-tiny /
- Ghost convolution

HTML全文

图 1 Ghost卷积

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图 2 改进YOLOv4-tiny的轻量化室内人员检测网络结构图

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图 3 Ghost 卷积特征提取网络模块

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图 4 多尺度空洞卷积融合模块结构图

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图 5 基于通道混洗机制的深度可分离卷积模块

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图 6 改进的ASPP结构

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图 7 Coordinate Attention模块结构图

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图 8 YOLOv4-tiny与本文算法在不同场景下检测效果对比图

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图 9 多场景下多指标综合对比图

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表 1 不同扩张率下实验结果

多尺度空洞卷积融合模块				空洞空间金字塔池化特征融合模块
扩张率	精确率(%)	召回率(%)	mAP(%)	扩张率	精确率(%)	召回率(%)	mAP(%)
[2,2,2]	79.69	68.29	81.75	[3,3,3]	79.49	78.97	82.91
[4,4,4]	79.55	67.95	81.15	[9,9,9]	78.95	79.21	82.49
[2,3,4]	76.18	80.43	82.93	[2,4,6]	80.32	78.56	82.66
[3,2,4]	79.33	78.96	82.52	[3,6,9]	76.18	80.43	82.93
[4,5,6]	79.35	78.62	82.12	[12,14,18]	79.46	77.66	82.46

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表 2 模块验证结果

	ghost block	CBLCS	ASPP	CA	dilated conv block	参数量(M)	FLOPs(G)	模型体积(MB)	精确率(%)	召回率(%)	mAP(%)
模型A	√					1.23	0.92	5.5	80.76	57.92	75.28
模型B	√	√				1.22	1.02	5.6	79.68	62.74	77.14
模型C	√	√	√			1.44	1.05	6.3	81.34	64.28	79.33
模型D	√	√	√	√		1.44	1.05	6.5	80.09	69.19	81.13
模型E	√	√	√	√	√	1.61	1.46	6.4	76.18	80.43	82.93

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表 3 多个数据集下检测效果对比(%)

数据集名称	评价指标	YOLOv4-tiny	本文算法
PASCAL VOC Person数据集	精确率	76.73	76.18
	召回率	62.83	80.43
	mAP	74.63	82.93
INRIA数据集	精确率	90.81	98.13
	召回率	75.00	79.23
	mAP	88.86	91.74
CUHK Occlusion 数据集	精确率	90.97	89.71
	召回率	73.85	72.82
	mAP	82.47	86.03
机房环境自建数据集	精确率	74.68	95.82
	召回率	96.31	88.36
	mAP	95.72	93.84

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表 4 不同网络模型结果对比

模型类型	模型名称	参数量(M)	FLOPs(G)	模型体积(MB)	精确率(%)	召回率(%)	mAP(%)
通用目标检测网络	YOLOv4^[10]	64.36	30.16	277.7	76.21	84.53	86.63
	SSD^[3]	26.15	59.52	90.7	69.37	71.18	72.15
	EfficientDet^[4]	3.87	2.55	14.9	79.84	70.82	82.17
轻量化网络	YOLOv4-tiny^[9]	5.91	3.43	22.5	76.73	62.83	74.63
	MobileNet-SSDv2^[22]	6.07	1.55	14.5	76.31	64.55	75.86
	YOLOv4-MobileNet v1^[23]	12.26	4.98	51.4	75.12	80.26	81.96
	YOLOv4-MobileNet v2^[24]	10.37	3.78	46.8	75.97	80.00	82.96
	YOLOv4-MobileNet v3^[25]	11.30	3.51	54.1	70.97	73.85	82.47
	YOLOv4-GhostNet^[26]	11.00	3.25	42.7	77.45	78.01	83.10
	本文算法	1.61	1.46	6.4	76.18	80.43	82.93

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表 5 不同性能设备推理速度对比

模型类型	模型名称	fps(帧/s)				帧图片推理耗时(ms)
模型类型	模型名称	GPU环境 RTX2070	CPU环境 I5-8200U	Jetson Nx	Jetson Nano	GPU环境 RTX2070	CPU环境 I5-8200U	Jetson Nx	Jetson Nano
通用目标检测网络	YOLOv4^[10]	26	0.02	5.17	1.46	38	49710	193	680
	SSD^[3]	69	0.35	10.80	2.86	14	2853	917	349
	EfficientDet^[4]	18	0.14	4.80	3.46	54	7022	207	288
轻量化网络	YOLOv4-tiny^[9]	101	4.01	24.00	12.48	9.90	249	40	80
	Mobilenet-SSDv2^[22]	76	2.33	19.00	14.47	13	425	504	69
	YOLOv4-MobileNet v1^[23]	50	1.20	15.30	5.03	19	827	65	198
	YOLOv4-MobileNet v2^[24]	44	1.17	13.20	5.25	22	849	75	190
	YOLOv4-MobileNet v3^[25]	37	1.26	11.90	5.51	26	792	83	181
	YOLOv4-GhostNet^[26]	30	1.27	9.70	4.20	33	786	102	238
	本文算法	105	9.01	27.00	16.01	9.52	115	37	62

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