Journal of Computer Applications ›› 2023, Vol. 43 ›› Issue (4): 1291-1296.DOI: 10.11772/j.issn.1001-9081.2022020313
Special Issue: 多媒体计算与计算机仿真
• Multimedia computing and computer simulation • Previous Articles Next Articles
Zhouhua ZHU, Qi QI()
Received:
2022-03-18
Revised:
2022-07-14
Accepted:
2022-07-18
Online:
2022-08-16
Published:
2023-04-10
Contact:
Qi QI
About author:
ZHU Zhouhua, born in 1976, M. S., associate professor. Her research interests include machine learning, object detection, digital signal processing.
Supported by:
通讯作者:
齐琦
作者简介:
朱周华(1976—),女,陕西西安人,副教授,硕士,主要研究方向:机器学习、目标检测、数字信号处理;
基金资助:
CLC Number:
Zhouhua ZHU, Qi QI. Automatic detection and recognition of electric vehicle helmet based on improved YOLOv5s[J]. Journal of Computer Applications, 2023, 43(4): 1291-1296.
朱周华, 齐琦. 基于改进YOLOv5s电动车头盔的自动检测与识别[J]. 《计算机应用》唯一官方网站, 2023, 43(4): 1291-1296.
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URL: https://www.joca.cn/EN/10.11772/j.issn.1001-9081.2022020313
类别 | 精度 | 召回率 | mAP0.5 | |||
---|---|---|---|---|---|---|
改进前 | 改进后 | 改进前 | 改进后 | 改进前 | 改进后 | |
平均值 | 79.3 | 88.2 | 83.7 | 85.3 | 84.2 | 91.3 |
electric | 84.2 | 91.6 | 91.5 | 91.5 | 93.1 | 96.9 |
helmet | 86.2 | 90.5 | 88.5 | 92.6 | 91.1 | 96.2 |
no helmet | 67.5 | 82.4 | 71.1 | 71.8 | 68.4 | 80.8 |
Tab. 1 Performance comparison of YOLOv5s before and after improvement
类别 | 精度 | 召回率 | mAP0.5 | |||
---|---|---|---|---|---|---|
改进前 | 改进后 | 改进前 | 改进后 | 改进前 | 改进后 | |
平均值 | 79.3 | 88.2 | 83.7 | 85.3 | 84.2 | 91.3 |
electric | 84.2 | 91.6 | 91.5 | 91.5 | 93.1 | 96.9 |
helmet | 86.2 | 90.5 | 88.5 | 92.6 | 91.1 | 96.2 |
no helmet | 67.5 | 82.4 | 71.1 | 71.8 | 68.4 | 80.8 |
算法 | 精度 | 召回率 | mAP0.5 |
---|---|---|---|
文献[ | 96.0 | 73.0 | 85.0 |
本文算法 | 88.2 | 86.4 | 93.1 |
Tab. 2 Performance comparison of different algorithms
算法 | 精度 | 召回率 | mAP0.5 |
---|---|---|---|
文献[ | 96.0 | 73.0 | 85.0 |
本文算法 | 88.2 | 86.4 | 93.1 |
模型 | 精度 | 召回率 | mAP0.5 |
---|---|---|---|
原始YOLOv5s | 79.3 | 83.7 | 84.2 |
YOLOv5s+注意力机制 | 81.6 | 84.5 | 86.8 |
YOLOv5s+DIoU-NMS | 80.1 | 84.1 | 86.4 |
YOLOv5s+多尺度特征融合 | 80.8 | 83.9 | 87.6 |
Tab. 3 YOLOv5s ablation experiment results
模型 | 精度 | 召回率 | mAP0.5 |
---|---|---|---|
原始YOLOv5s | 79.3 | 83.7 | 84.2 |
YOLOv5s+注意力机制 | 81.6 | 84.5 | 86.8 |
YOLOv5s+DIoU-NMS | 80.1 | 84.1 | 86.4 |
YOLOv5s+多尺度特征融合 | 80.8 | 83.9 | 87.6 |
算法 | 精度 | 召回率 | mAP0.5 |
---|---|---|---|
YOLOv5s | 79.3 | 83.7 | 84.2 |
YOLOv5m | 83.6 | 85.6 | 88.2 |
YOLOv5l | 84.8 | 86.1 | 90.1 |
YOLOv5x | 84.1 | 85.5 | 89.4 |
改进YOLOv5s | 88.2 | 86.4 | 91.3 |
Tab. 4 Performance comparison between the proposed algorithm and YOLOv5 series algorithms
算法 | 精度 | 召回率 | mAP0.5 |
---|---|---|---|
YOLOv5s | 79.3 | 83.7 | 84.2 |
YOLOv5m | 83.6 | 85.6 | 88.2 |
YOLOv5l | 84.8 | 86.1 | 90.1 |
YOLOv5x | 84.1 | 85.5 | 89.4 |
改进YOLOv5s | 88.2 | 86.4 | 91.3 |
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