Journal of Computer Applications ›› 2022, Vol. 42 ›› Issue (4): 1292-1300.DOI: 10.11772/j.issn.1001-9081.2021071246
• The 36 CCF National Conference of Computer Applications (CCF NCCA 2020) • Previous Articles
Jin ZHANG1, Peiqi QU1(), Cheng SUN2, Meng LUO2
Received:
2021-07-16
Revised:
2021-08-27
Accepted:
2021-08-31
Online:
2021-09-10
Published:
2022-04-10
Contact:
Peiqi QU
About author:
ZHANG Jin, born in 1979, Ph. D., professor. His research interests include artificial intelligence, software engineering.Supported by:
通讯作者:
屈佩琪
作者简介:
张锦(1979—),男,河南信阳人,教授,博士,CCF会员,主要研究方向:人工智能、软件工程基金资助:
CLC Number:
Jin ZHANG, Peiqi QU, Cheng SUN, Meng LUO. Safety helmet wearing detection algorithm based on improved YOLOv5[J]. Journal of Computer Applications, 2022, 42(4): 1292-1300.
张锦, 屈佩琪, 孙程, 罗蒙. 基于改进YOLOv5的安全帽佩戴检测算法[J]. 《计算机应用》唯一官方网站, 2022, 42(4): 1292-1300.
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URL: http://www.joca.cn/EN/10.11772/j.issn.1001-9081.2021071246
特征图尺度 | 锚框尺寸 | ||
---|---|---|---|
锚框1 | 锚框2 | 锚框3 | |
小尺度 | (11.9,18) | (21.5,30.8) | (30.8,43) |
中尺度 | (38.1,60) | (52.3,73.6) | (63,103.3) |
大尺度 | (89.2,135) | (120,207.5) | (209.4,324) |
Tab. 1 Anchor box size
特征图尺度 | 锚框尺寸 | ||
---|---|---|---|
锚框1 | 锚框2 | 锚框3 | |
小尺度 | (11.9,18) | (21.5,30.8) | (30.8,43) |
中尺度 | (38.1,60) | (52.3,73.6) | (63,103.3) |
大尺度 | (89.2,135) | (120,207.5) | (209.4,324) |
K-Means++聚类 | MCA模块 | AP50/% | mAP/% | |
---|---|---|---|---|
佩戴安全帽 | 未佩戴安全帽 | |||
× | × | 93.3 | 91.7 | 92.7 |
√ | × | 94.4 | 92.8 | 93.6 |
× | √ | 95.6 | 94.4 | 95.0 |
√ | √ | 96.7 | 95.2 | 96.0 |
Tab. 2 Performance comparison of different improvements of YOLOv5
K-Means++聚类 | MCA模块 | AP50/% | mAP/% | |
---|---|---|---|---|
佩戴安全帽 | 未佩戴安全帽 | |||
× | × | 93.3 | 91.7 | 92.7 |
√ | × | 94.4 | 92.8 | 93.6 |
× | √ | 95.6 | 94.4 | 95.0 |
√ | √ | 96.7 | 95.2 | 96.0 |
检测算法 | AP50 | 精度 | 召回率 | mAP | ||
---|---|---|---|---|---|---|
小目标 | 中等目标 | 大目标 | ||||
YOLOv5 | 83.0 | 97.9 | 99.3 | 76.4 | 92.5 | 92.7 |
MCA-YOLOv5-BackBone | 90.4 | 98.6 | 99.6 | 82.2 | 95.4 | 96.0 |
MCA-YOLOv5-Neck | 78.3 | 96.4 | 99.1 | 70.9 | 93.7 | 91.6 |
MCA-YOLOv5-Prediction | 82.7 | 97.1 | 99.2 | 72.5 | 92.8 | 92.4 |
Tab. 3 Comparison of MCA module fusion results
检测算法 | AP50 | 精度 | 召回率 | mAP | ||
---|---|---|---|---|---|---|
小目标 | 中等目标 | 大目标 | ||||
YOLOv5 | 83.0 | 97.9 | 99.3 | 76.4 | 92.5 | 92.7 |
MCA-YOLOv5-BackBone | 90.4 | 98.6 | 99.6 | 82.2 | 95.4 | 96.0 |
MCA-YOLOv5-Neck | 78.3 | 96.4 | 99.1 | 70.9 | 93.7 | 91.6 |
MCA-YOLOv5-Prediction | 82.7 | 97.1 | 99.2 | 72.5 | 92.8 | 92.4 |
目标类别 | 训练集目标数 | 测试集目标数 | 标注目标总数 |
---|---|---|---|
佩戴安全帽类别 | 81 836 | 11 316 | 93 152 |
未佩戴安全帽类别 | 98 187 | 12 021 | 110 208 |
Tab. 4 Dataset category distribution
目标类别 | 训练集目标数 | 测试集目标数 | 标注目标总数 |
---|---|---|---|
佩戴安全帽类别 | 81 836 | 11 316 | 93 152 |
未佩戴安全帽类别 | 98 187 | 12 021 | 110 208 |
类别 | 条目 | 版本 |
---|---|---|
硬件配置 | 显卡 | GeForce RTX 2080 Ti |
软件配置 | 系统 | Ubuntu 18.04 |
CPU | AMD Ryzen 7 3800X 8-Core | |
Python版本 | 3.8 | |
深度学习框架 | Pytorch | |
CUDA | 10.0 |
Tab. 5 Experimental operating environment
类别 | 条目 | 版本 |
---|---|---|
硬件配置 | 显卡 | GeForce RTX 2080 Ti |
软件配置 | 系统 | Ubuntu 18.04 |
CPU | AMD Ryzen 7 3800X 8-Core | |
Python版本 | 3.8 | |
深度学习框架 | Pytorch | |
CUDA | 10.0 |
检测算法 | AP50/% | mAP/% | 参数量/106 | 推理时间/ms | 模型大小/MB | |
---|---|---|---|---|---|---|
佩戴安全帽 | 未佩戴安全帽 | |||||
Faster R-CNN | 80.80 | 42.20 | 61.50 | 186.00 | 291 | 182.1 |
SSD | 78.80 | 68.20 | 73.50 | 23.75 | 126 | 188.0 |
YOLOv3 | 89.12 | 80.70 | 84.90 | 61.90 | 69 | 236.0 |
YOLOv3+SPP | 90.50 | 86.30 | 88.41 | 63.00 | 70 | 237.4 |
YOLOv5 | 93.30 | 91.70 | 92.70 | 7.10 | 36 | 14.8 |
MCA-YOLOv5 | 96.70 | 95.20 | 96.00 | 7.30 | 37 | 15.2 |
Tab. 6 Comparison of results of multiple detection algorithms
检测算法 | AP50/% | mAP/% | 参数量/106 | 推理时间/ms | 模型大小/MB | |
---|---|---|---|---|---|---|
佩戴安全帽 | 未佩戴安全帽 | |||||
Faster R-CNN | 80.80 | 42.20 | 61.50 | 186.00 | 291 | 182.1 |
SSD | 78.80 | 68.20 | 73.50 | 23.75 | 126 | 188.0 |
YOLOv3 | 89.12 | 80.70 | 84.90 | 61.90 | 69 | 236.0 |
YOLOv3+SPP | 90.50 | 86.30 | 88.41 | 63.00 | 70 | 237.4 |
YOLOv5 | 93.30 | 91.70 | 92.70 | 7.10 | 36 | 14.8 |
MCA-YOLOv5 | 96.70 | 95.20 | 96.00 | 7.30 | 37 | 15.2 |
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