| 马晓东,魏利胜,刘小珲.基于新型 YOLO v5 算法的磁悬浮球精确识别[J].电子测量与仪器学报,2022,36(8):204-212 |
| 基于新型 YOLO v5 算法的磁悬浮球精确识别 |
| Accurate identification of magnetic levitation ballbased on novel YOLOv5 algorithm |
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| DOI： |
| 中文关键词: 磁悬浮 YOLOv5 新型 注意力机制 CIOU |
| 英文关键词:magnetic levitation YOLOv5 novel coordinate attention CIOU |
| 基金项目:安徽省教育厅重大项目(KJ2020ZD39)、安徽省检测技术与节能装置重点实验室开放基金项目(DTESD2020A02)资助 |
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| 中文摘要: |
| 针对磁悬浮控制系统中目标物体定位精度低以及速度慢的问题,提出一种基于 YOLOv5(you only look once v5)改进算
法来对磁悬浮球进行识别定位。 首先,利用 Mish 损失函数取代 YOLOv5 原模型中 SiLU(sigmoid-weighted linear units)激活函数,
以得到准确性更高和泛化能力更强的网络模型;其次,将协同注意力机制融合到 YOLOv5 算法中,提高模型的特征提取能力;在
此基础上,选择 CIOU(complete-intersection over union)损失函数替换 YOLOv5 算法中的 GIOU(generalized intersection over union)
损失函数来优化训练模型,以提高识别精度。 最后,进行了仿真验证,结果表明,改进后的 YOLOv5 算法与原算法相比,在磁悬
浮球目标识别精度由原来的 92. 4%提高到 96. 2%,MAP(mean average precision)由原来的 88. 8%提高到 94. 3%,从而验证了本
文所提方法的有效性和可行性。 |
| 英文摘要: |
| Aiming at the problems of low positioning accuracy and slow speed of target objects in the magnetic levitation control system,
a novel YOLOv5 (you only look once v5) algorithm was proposed to identify and locate the magnetic levitation ball. Firstly, by using the
Mish loss function to replace the SiLU ( sigmoid-weighted linear units) activation function of YOLOv5 model, the higher accuracy and
stronger generalization network model could be obtained. Then fusing the coordinate attention module into YOLOv5, the feature
extraction capability of the model could be improved. On this basis, the CIOU ( complete-intersection over union) loss function was
selected to replace the GIOU ( generalized intersection over union) loss function to improve the identification accuracy. Finally, the
simulation verification was carried out. The results showed that the improved YOLOv5 algorithm could improve the target recognition
accuracy of the magnetic levitation ball from 92. 4% to 96. 2%, and the MAP (mean average precision) from the original 88. 8% to
94. 3%. Therefore, the effectiveness and feasibility of the proposed method could be verified. |
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