| 陈天亮,王强,许卫荣,吴琳琳,徐晓萌,周海婷.基于改进YoloX算法的HDPE管接头TFM相控阵超声图谱缺陷识别[J].声学技术,2024,43(4):495~502 |
| 基于改进YoloX算法的HDPE管接头TFM相控阵超声图谱缺陷识别 |
| Defect identification of HDPE pipe joint with TFM phased array ultrasonic spectrum based on improved YoloX algorithm |
| 投稿时间：2022-12-05 修订日期：2023-02-16 |
| DOI：10.16300/j.cnki.1000-3630.2024.04.007 |
| 中文关键词: 聚乙烯管热熔接头 全聚焦相控阵 缺陷识别 深度学习 |
| 英文关键词: polyethylene pipe thermal butt fusion joint total focus phased array defects identification deep learning |
| 基金项目:浙江省“尖兵”“领雁”研发攻关计划(2022C03179);浙江省市场监督管理局科技计划项目(20210144);浙江省“万人计划”科技创新领军人才项目(2019R52017)。 |
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| 中文摘要: |
| 针对高密度聚乙烯(High Density Polyethylene, HDPE)管热熔接头相控阵超声检测时存在的图谱判读效率低、人员经验要求高等问题，文章提出一种基于改进YoloX算法的热熔接头全聚焦(Total Focusing Method, TFM)相控阵超声图谱缺陷(以孔洞为例)智能识别方法。在YoloX的加强特征提取网络中引入卷积注意力机制模块，提高模型对缺陷信息的关注度，使用CIoU损失函数计算回归损失，以提升模型的定位精度，降低漏检率。通过TFM相控阵超声检测实验，采集原始缺陷图谱，并在完成图像增强后创建数据集。采用迁移学习策略进行训练，加快模型收敛速度。结果表明：该方法对缺陷的识别精度达98.18%，检测平均速度达23.92帧·s-1，检测精度相较于原YoloX模型提升了2.57个百分点且对小目标缺陷有更好的检测效果。文中方法可以识别出TFM相控阵超声图谱中的缺陷，为热熔接头的精确检测提供技术支撑。 |
| 英文摘要: |
| In the current phased-array ultrasonic inspection of high-density polyethylene (HDPE) pipe thermal butt fusion joint, there are some problems such as low efficiency of pattern interpretation and high personnel experience requirements. Based on the improved YoloX algorithm, an intelligent defect (taking holes as an example) identification method for the TFM phased array ultrasonic maps of thermal butt fusion joint is proposed in this paper. The convolutional block attention module (CBAM) is introduced into the path aggregation network (PAnet) of YoloX to improve the model attention to defect information. The CIoU loss function is used to calculate regression loss to improve the positioning accuracy of the model and reduce the missing detection probability. By TFM phased array ultrasonic testing experiment, the original defect maps are gathered, and the data set is created after image enhancement. The transfer learning strategy is adopted for training to speed up the convergence of the model. The results show that the recognition accuracy of the method for defects reaches 98.18% and the average speed of detection reaches 23.92 frames per second, which improves the detection accuracy by 2.57 percentage points compared with that of the original YoloX model, and has a better detection effect on small target defects. The method proposed in this paper can identify the defects in the TFM phased array ultrasonic spectrum, which can provide technical support for the accurate detection of thermal butt fusion joint. |
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