| 代啟亮,熊 凌,陈琳国,李姝凡.改进 YOLOv5 的 PDC 钻头复合片缺损识别[J].电子测量与仪器学报,2023,37(8):164-172 |
| 改进 YOLOv5 的 PDC 钻头复合片缺损识别 |
| PDC drill bit defect recognition by improved YOLOv5 |
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| DOI： |
| 中文关键词: PDC 钻头复合片 YOLOv5 RepVGG 坐标注意力机制 WIoU 损失函数 |
| 英文关键词:PDC drill bit composite YOLOv5 RepVGG coordinate attention mechanism WIoU loss function |
| 基金项目:国家自然科学基金(62173261)、湖北省重点研发计划项目(2020BAB021)资助 |
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| 中文摘要: |
| PDC 钻头复合片的缺损情况是影响钻进效率的重要因素,检测 PDC 钻头复合片是否缺损是修复 PDC 钻头的前提。 为
了减少对 PDC 钻头复合片的误检,提升检测准确率,提出了一种基于改进 YOLOv5 的目标检测算法。 该方法以 YOLOv5 网络为
基础,融合 RepVGG 重参数化模块增强网络的特征提取能力;在 C3 模块中引入坐标注意力机制,在通道注意力机制中嵌入位置
信息,提升对缺损复合片的目标检测能力;将边界框回归损失函数改进为 WIoU 损失函数,制定合适的梯度增益分配策略。 实
验结果表明,改进后的网络的精确率提升 2%,召回率提升 0. 9%,平均精度均值(mAP)提升了 1. 3%,达到了 98%,能够实现对
PDC 钻头复合片的缺损识别。 |
| 英文摘要: |
| The defect of the PDC bit compact is an important factor affecting the drilling efficiency, and detecting whether the PDC bit
compact is defective is a prerequisite for repairing the PDC bit. In order to reduce the false detection of PDC drill bit composites and
improve the detection accuracy, a target detection algorithm based on improved YOLOv5 is proposed. This method is based on the
YOLOv5 network, and integrates the RepVGG reparameterization module to enhance the feature extraction ability of the network;
introduces the coordinate attention mechanism in the C3 module, embeds the position information in the channel attention mechanism,
and improves the target detection ability of the defective composite film. Improve the bounding box regression loss function to the WIoU
loss function, and formulate a suitable gradient gain allocation strategy. The experimental results show that the precision rate of the
improved network increased with 2%, the recall rate increased with 0. 9%, and the mean average precision ( mAP) increased with
1. 3%, reaching 98%, which can realize the defect recognition of PDC drill bit composites. |
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