轻量型YOLOv5s车载红外图像目标检测

刘彦磊; 李孟喆; 王宣宣

doi:10.37188/CO.2022-0254

轻量型YOLOv5s车载红外图像目标检测

doi: 10.37188/CO.2022-0254

河南师范大学物理学院, 河南省红外材料光谱测量与应用重点实验室, 河南新乡 453007

基金项目: 国家自然科学基金（No. 61905068）

详细信息

作者简介:
刘彦磊（1986—），男，河南中牟人，博士，讲师，2011年，2014年于河南师范大学分别获得学士、硕士学位，2018年6月于北京理工大学获得博士学位，主要从事红外光谱测量及应用技术方面的研究。E-mail：liuyanlei@htu.edu.cn

中图分类号: TP391.4
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出版历程
- 收稿日期: 2022-12-14
- 修回日期: 2023-01-06
- 录用日期: 2023-03-24
- 网络出版日期: 2023-04-13

Lightweight YOLOv5s vehicle infrared image target detection

Henan Key Laboratory of Infrared Materials & Spectrum Measures and Applications, College of Physics, Henan Normal University, Xinxiang 453007, China

Funds: Supported by National Natural Science Foundation of China (No. 61905068)

More Information

Corresponding author: liuyanlei@htu.edu.cn

摘要

摘要:
车载红外图像的目标检测是自动驾驶进行道路环境感知的重要方式。针对现有车载红外图像目标检测算法中内存利用率低、计算复杂和检测精度低的问题，提出了一种改进YOLOv5s的轻量型目标检测算法。首先，将C3Ghost和Ghost模块引入YOLOv5s检测网络，以降低网络复杂度。其次，引进αIoU损失函数，以提升目标的定位精度和训练效率。然后，降低网络结构下采样率，并利用KMeans聚类算法优化先验框大小，以提高小目标检测能力。最后，分别在主干网络和颈部引入坐标注意力（Coordinate Attention，CA）和空间深度卷积模块进一步优化模型，提升模型特征的提取能力。实验结果表明，相对于原YOLOv5s算法，改进算法的模型大小压缩78.1%，参数量和每秒千兆浮点运算数分别减少84.5%和40.5%，平均检测精度和检测速度分别提升4.2%和10.9%。
- 自动驾驶 /
- 目标检测 /
- 红外图像 /
- 轻量型 /
- YOLOv5s
Abstract:
Vehicle infrared image target detection is an important way of road environment perception for autonomous driving. However, existing vehicle infrared image target detection algorithms have defects, such as low memory utilization, complex calculation and low detection accuracy. In order to solve the above problems, an improved YOLOv5s lightweight target detection algorithm is proposed. Firstly, the C3Ghost and Ghost modules are introduced into the YOLOv5s detection network to reduce network complexity. Secondly, the αIoU loss function is introduced to improve the positioning accuracy of the target and the networks training efficiency. Then, the subsampling rate of the network structure is reduced and the KMeans clustering algorithm is used to optimize the prior anchor size to improve the ability to detect of small targets. Finally, coordinate attention and spatial depth convolution modules are respectively introduced into the Backbone and Neck to further optimize the model and improve the feature extraction of the model. The experimental results show that compared with the original YOLOv5s algorithm, the improved algorithm can compress the model size by 78.1%, reduce the number of parameters and Giga Floating-point Operations Per Second by 84.5% and 40.5% respectively, and improve the mean average precision and detection speed by 4.2% and 10.9%, respectively.
- autonomous driving /
- target detection /
- infrared image /
- lightweight /
- YOLOv5s

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图 1 YOLOv5s算法结构

Figure 1. YOLOv5s algorithm structure

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图 2 改进YOLOv5s算法结构

Figure 2. Improved YOLOv5s algorithm structure

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图 3 （a）普通卷积和（b）Ghost卷积（Φ为线性操作）

Figure 3. (a) Ordinary convolution and (b) Ghost convolution (Φ is a linear operation)

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图 4 CA结构

Figure 4. CA structure

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图 5 空间深度卷积（Scale=2）

Figure 5. SPD-Conv (Scale=2)

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图 6 数据增强结果。（a）Mosaic增强；（b）MixUp增强；（c）Copy-Paste增强

Figure 6. Data augmentation results. (a) Mosaic augmentation; (b) MixUp augmentation; (c) Copy-Paste augmentation

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图 7 几种不同算法的检测效果。（a）YOLOv3-tiny；（b）YOLOv4-tiny；（c）YOLOv5n；（d）YOLOv6-N；（e）YOLO7-tiny；（f）YOLO5s；（g）本文算法

Figure 7. Detection results of different algorithms. (a) YOLOv3-tiny; (b) YOLOv4-tiny; (c) YOLOv5n; (d) YOLOv6-N; (e) YOLO7-tiny; (f) YOLO5s; (g) proposed in this paper

下载: 全尺寸图片幻灯片

表 1 优化后先验框大小

Table 1. Optimized prior anchor size

特征图尺度	160×160	80×80	40×40
感受野大小	小	中	大
	[6,8]	[14,37]	[35,94]
先验框	[7,19]	[31,26]	[96,68]
	[15,13]	[50,37]	[154,145]

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表 2 YOLOv5s和YOLOv5s-G轻量化性能对比

Table 2. Performance comparison of lightweight for YOLOv5s and YOLOv5s-G

Model	t/hours	Size/MB	Params/M	GFLOPs	P(%)	R(%)	mAP(%)	FPS
YOLOv5s	48.77	13.70	7.02	15.8	87.1	69.8	80.8	119
YOLOv5s-G	30.25	7.46	3.68	8.0	86.1	66.3	77.5	137

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表 3 不同损失函数性能对比

Table 3. Performance comparison of different loss functions

Model	t/hours	P(%)	R(%)	mAP(%)	FPS
YOLOv5s-G	30.25	86.1	66.3	77.5	137
YOLOv5s-G-EIoU	24.31	84.5	68.7	78.9	141
YOLOv5s-G-SIoU	24.62	85.8	67.2	77.8	139
YOLOv5s-G-αIoU	23.50	85.9	69.3	79.8	147

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表 4 多尺度融合性能对比

Table 4. Performance comparison of multi-scale fusion

Model	t/hours	Size/MB	Params/M	GFLOPs	P(%)	R(%)	mAP(%)	FPS
YOLOv5s-G-αIoU	23.50	7.46	3.68	8.0	85.9	69.3	79.8	147
YOLOv5s-G1-αIoU	26.89	8.60	3.75	9.6	86.0	73.6	83.6	125
YOLOv5s-G2-αIoU	24.56	2.73	0.95	7.2	84.5	72.8	82.9	154
YOLOv5s-G2-αIoU-KMeans	25.62	2.73	0.95	7.2	85.5	72.4	83	154

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表 5 不同注意力机制性能对比

Table 5. Performance comparison of different attention mechanisms

Model	t/hours	Size/MB	Params/M	GFLOPs	P(%)	R(%)	mAP(%)	FPS
YOLOv5s-G2-αIoU-KMeans	25.62	2.73	0.95	7.2	85.5	72.4	83	154
YOLOv5s-G2-αIoU-KMeans-SE	30.95	2.75	0.96	7.2	86.0	73.5	84.1	149
YOLOv5s-G2-αIoU-KMeans-ECA	26.06	2.73	0.95	7.2	85.5	73.8	84.2	145
YOLOv5s-G2-αIoU-KMeans-CBAM	28.21	2.76	0.96	7.3	85.7	73.4	84	135
YOLOv5s-G2-αIoU-KMeans-CA	28.62	2.76	0.96	7.3	86.6	73.6	84.3	139

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表 6 空间深度卷积效果

Table 6. SPD-Conv effect

Model	t/hours	Size/MB	Params/M	GFLOPs	P(%)	R(%)	mAP(%)	FPS
YOLOv5s-G2-αIoU-Kmeans-CA	28.62	2.76	0.96	7.3	86.6	73.6	84.3	139
YOLOv5s-G2-αIoU-Kmeans-CA-SPD	30.28	3.0	1.09	9.4	87.4	74.6	85.0	132

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表 7 与其他先进算法对比

Table 7. Comparison with other advanced algorithms

Model	Size/MB	Params/M	GFLOPs	P(%)	R(%)	mAP(%)	FPS
SSD	186.0	23.70	115.7	68.9	55.7	63.2	88
EfficientDet	302.0	39.40	107.5	72.8	58.4	67.8	52
YOLOv4+GhostNet	150.3	39.30	25.6	81.1	66.9	77.7	112
YOLOv5-MobileNetV3	7.9	4.0	9.3	83.7	67.5	76.9	128
YOLOv3-tiny	16.6	8.67	12.9	79.3	54.9	62.9	175
YOLOv4-tiny	12.9	6.27	16.2	78.9	57.3	67.2	149
YOLOv5n	3.7	1.76	5.1	83.6	66.1	76.6	164
YOLOv6-N	9.3	4.30	11.1	84.8	71.5	80.3	208
YOLOv7-tiny	12.3	6.02	13.2	84.2	74.7	83.6	143
YOLOv5s	13.7	7.02	15.8	87.1	69.8	80.8	119
proposed in this paper	3.0	1.09	9.4	87.4	74.6	85.0	132

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