改进YOLOv5网络的轻量级服装目标检测方法

doi:10.13475/j.fzxb.20210809306

纺织学报 ›› 2022, Vol. 43 ›› Issue (10): 155-160.doi: 10.13475/j.fzxb.20210809306

改进YOLOv5网络的轻量级服装目标检测方法

陈金广¹(), 李雪¹, 邵景峰², 马丽丽¹

1.西安工程大学计算机科学学院, 陕西西安 710048
2.西安工程大学管理学院, 陕西西安 710048

收稿日期:2021-08-24 修回日期:2022-06-27 出版日期:2022-10-15 发布日期:2022-10-28
作者简介:陈金广(1977—),男,教授,博士。主要研究方向为信息融合、机器学习、计算机应用。E-mail: xacjg@163.com。
基金资助:
陕西省重点研发计划项目(2020GY-122);陕西省教育厅科研计划项目(21JP049);西安市科技计划项目(2020KJRC0018);西安工程大学研究生创新基金项目(chx2021026)

Lightweight clothing detection method based on an improved YOLOv5 network

CHEN Jinguang¹(), LI Xue¹, SHAO Jingfeng², MA Lili¹

1. School of Computer Science, Xi'an Polytechnic University, Xi'an, Shaanxi 710048, China
2. School of Management, Xi'an Polytechnic University, Xi'an, Shaanxi 710048, China

Received:2021-08-24 Revised:2022-06-27 Published:2022-10-15 Online:2022-10-28

摘要/Abstract

摘要：

为进一步降低基于深度学习的服装目标检测模型对计算资源的占用,提出一种改进的轻量级服装目标检测方法MV3L-YOLOv5。首先使用移动网络MobileNetV3_Large构造YOLOv5的主干网络;然后在训练阶段使用标签平滑策略,以增强模型泛化能力;最后使用数据增强技术弥补DeepFashion2数据集中不同服装类别图像数量不均衡问题。实验结果表明:MV3L-YOLOv5的模型体积为10.27 MB,浮点型计算量为10.2×10⁹次,平均精度均值为76.6%。与YOLOv5系列最轻量的YOLOv5s网络相比,模型体积压缩了26.4%,浮点型计算量减少了39%,同时平均精度均值提高了1.3%。改进后的算法在服装图像的目标检测方面效果有所提升,且模型更加轻量,适合部署在资源有限的设备中。

关键词: 深度学习, 目标检测, 服装图像, 轻量级网络, YOLOv5

Abstract:

In order to further reduce the occupation of computing resources by the clothing object detection model based on deep learning, an improved lightweight clothing object detection method, MV3L-YOLOv5, was proposed. The MobileNetV3_Large is used to construct the backbone network of YOLOv5, and the label smoothing strategy was introduced to enhance the generalization ability at the training stage of the model. The data augmentation technology was used to make up for the unbalanced number of images of different clothing categories in the DeepFashion2 dataset. Experimental results show that the model volume of MV3L-YOLOv5 is 10.27 MB, the floating-point operations is 10.2×10⁹ times, and mean average precision is 76.6 %. Comparing with YOLOv5s, which is the lightest network in YOLOv5 series, MV3L-YOLOv5 is compressed in the model volume by 26.4 %, reduced the floating-point operations by 39 %, and improved accuracy by 1.3 %. Experimental results in the improved algorithm show that the detection performance is notably improved, and the model is lighter and more suitable for deployment in devices with limited resources.

Key words: deep learning, object detection, clothing image, lightweight network, YOLOv5

中图分类号:

TS941

陈金广, 李雪, 邵景峰, 马丽丽. 改进YOLOv5网络的轻量级服装目标检测方法[J]. 纺织学报, 2022, 43(10): 155-160.

CHEN Jinguang, LI Xue, SHAO Jingfeng, MA Lili. Lightweight clothing detection method based on an improved YOLOv5 network[J]. Journal of Textile Research, 2022, 43(10): 155-160.

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参考文献 12

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服装类别	类别序号	训练集/个	验证集/个
短袖衫	1	4 028	2 337
长袖衫	2	2 147	1 130
短袖外衫	3	899	142
长袖外套	4	1 356	919
背心	5	1 596	991
吊带	6	1 110	322
短裤	7	2 833	1 186
长裤	8	3 691	2 265
半身裙	9	2 391	1 514
短袖连衣裙	10	1 132	516
长袖连衣裙	11	1 472	605
无袖连衣裙	12	1 357	846
吊带裙	13	846	504

服装类别	平均精度P_A/%
服装类别	MV3L-YOLOv5	YOLOv5s
短袖衫	91.5	91.4
长袖衫	76.9	75.3
短袖外衫	51.0	48.4
长袖外套	85.2	84.1
背心	83.5	84.1
吊带	64.0	61.8
短裤	89.1	89.5
长裤	94.1	94.2
半身裙	81.9	80.5
短袖连衣裙	64.0	63.6
长袖连衣裙	69.8	65.8
无袖连衣裙	69.9	69.2
吊带裙	74.8	70.3

模型	尺寸/像素	精准率/%	召回率/%	平均检测精度均值P_mA/%
MV3L-YOLOv5	640×640	72.1	72.8	76.6
YOLOv5s	640×640	72.8	69.4	75.3
YOLOv4-Tiny	640×640	34.5	81.8	66.8
YOLOv3-Tiny	640×640	60.3	62.2	62.5
YOLOv5l	640×640	75.1	74.7	79.4
YOLOv3-SPP	640×640	74.9	75.8	79.3

模型	参数量/ 10⁷ 个	浮点型计算量/ (10⁹次)	模型体积/ MB	推理时间/ms
MV3L-YOLOv5	5.2	10.2	10.27	13.3
YOLOv5s	7.2	16.7	13.94	7.4
YOLOv4-Tiny	6.1	12.9	24.16	5.7
YOLOv3-Tiny	8.7	13.0	16.65	5.3
YOLOv5l	47.1	115.2	90.15	25.3
YOLOv3-SPP	62.7	155.9	119.80	27.0

实验序号	模型	LS	Soft NMS	NMW	NMS
①	YOLOv5s	×	×	×	√
②	YOLOv5s	√	×	×	√
③	MobileNetV3_Small-YOLOv5	×	×	×	√
④	MobileNetV3_Small-YOLOv5	√	×	×	√
⑤	MobileNetV3_Large-YOLOv5	√	√	×	×
⑥	MobileNetV3_Large-YOLOv5	√	×	√	×
⑦	MobileNetV3_Large-YOLOv5	×	×	×	√
⑧	MV3L-YOLOv5(ours)	√	×	×	√

改进YOLOv5网络的轻量级服装目标检测方法

Lightweight clothing detection method based on an improved YOLOv5 network

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