Page 92 - 《应用声学）》2023年第5期

第 42 卷 第 5 期 Vol. 42, No. 5
2023 年 9 月 Journal of Applied Acoustics September, 2023

⋄ 研究报告 ⋄

改进的Faster-RCNN算法在聚乙烯管接头内部

缺陷检测中的应用

彭伊娟 1 王振超 1,2 张秋菊 1†

(1 江南大学机械工程学院 无锡 214122)
(2 罗森博格 (无锡) 管道技术有限公司 无锡 214161)
摘要：超声相控阵技术是目前聚乙烯管道热熔接头内部缺陷检测的一种主流方法。提出了基于注意力机制的
改进 Faster-RCNN 目标检测网络用于超声相控阵 D 扫图聚乙烯管接头内部缺陷检测。针对聚乙烯管道热熔
接头内部超声相控阵 D 扫图小缺陷较多、特征信息容易丢失的问题，将残差网络 (ResNet50) 与特征金字塔网
络 (FPN) 相结合作为骨干网络，并引入卷积注意力模块 (CBAM) 自适应细化特征。将 SSD 网络框架和 Faster-
RCNN 网络框架用于模型训练和测试，使用 VGG16、ResNet50、ResNet50+FPN、ACBM+ResNet50+FPN
作为骨干网络依次对超声相控阵聚乙烯管道热熔对接接头内部缺陷样本进行训练对比。结果表明，改进的
Faster-RCNN 网络模型在聚乙烯管接头内部缺陷检测和分类方面有明显改进，对小缺陷的检测性能有了显著
的提高。
关键词：缺陷检测；超声相控阵；卷积注意力模块；残差网络；特征金字塔
中图法分类号: TP183; O429 文献标识码: A 文章编号: 1000-310X(2023)05-0984-09
DOI: 10.11684/j.issn.1000-310X.2023.05.011

Application of improved Faster-RCNN algorithm in internal defect detection of

polyethylene pipe joint

PENG Yijuan 1 WANG Zhenchao 1,2 ZHANG Qiuju 1
(1 School of Mechanical Engineering, Jiangnan University, Wuxi 214122, China)
(2 Rothenberger Wuxi Pipe Technologies Co., Ltd., Wuxi 214161, China)

Abstract: Ultrasonic phased array technology is a mainstream method to detect the internal defects of
polyethylene (PE) pipe hot melt joint. In this paper, an improved Faster-RCNN target detection network
based on attention mechanism is proposed for ultrasonic phased array D-sweep PE pipe joint internal defect
detection. To solve the problem that there are many small defects in ultrasonic phased array D-sweep
pattern inside hot melt joint of PE pipeline and feature information is easy to be lost, residual network
(ResNet50) is combined with feature pyramid network (FPN) as the backbone network, and convolution block
attention module (CBAM) is introduced to self-refine feature. SSD network framework and Faster-RCNN
network framework were used for model training and testing. VGG16, ResNet50, ResNet50+FPN and
ACBM+ResNet50+FPN backbone networks were used as backbone networks to train and compare the
internal defect samples of ultrasonic phased array PE pipeline hot melt butt joint. The results show that the
improved Faster-RCNN network model has obvious improvement in the detection and classification of internal
defects of PE pipe joints, and the detection performance of small defects is significantly improved.

2022-06-13 收稿; 2022-08-02 定稿
作者简介: 彭伊娟 (1998– ), 女, 江苏南通人, 硕士研究生, 研究方向: 缺陷检测。
† 通信作者 E-mail: qjzhang123@qq.com