基于改进Faster R-CNN的焊缝缺陷检测方法
DOI:
作者:
作者单位:

1.油气藏地质及开发工程全国重点实验室·2.西南石油大学;3.国家管网集团西南管道有限责任公司

作者简介:

通讯作者:

中图分类号:

TP391.41

基金项目:

国家重点研发计划(2016YFC0802100);


Weld Defect Detection Based on Improved Faster R-CNN Method
Author:
Affiliation:

1.National Key Laboratory of Reservoir Geology and Development Engineering-Southwest Petroleum University;2.National Pipe Network Group Southwest Pipeline Co.

Fund Project:

  • 摘要
  • |
  • 图/表
  • |
  • 访问统计
  • |
  • 参考文献
  • |
  • 相似文献
  • |
  • 引证文献
  • |
  • 资源附件
  • |
  • 文章评论
    摘要:

    管道内部的焊缝缺陷是导致管道发生泄漏和破裂事故的主要原因,而X射线能够有效的检测到这些缺陷。然而,焊缝缺陷存在种类多、尺寸小和背景复杂等问题,影响检测精度。针对目前基于深度学习的焊缝缺陷检测模型对图像复杂背景和光照变化的适应性不足、小目标检测效果不佳的问题。在Faster R-CNN网络的主干网络上添加通道注意力机制和对残差块结构进行修改,并采用ROI align替换传统Faster R-CNN网络的ROI Pooling的改进模型。实验结果表明:改进后的Faster R-CNN网络模型与原算法相比,平均精度值(mean Average Precision,mAP)和F1值(F1 score)分别与原算法提升了15.82%和16.44%,能够满足焊缝缺陷检测的高精度要求,具有重要的理论意义与良好的工程应用前景。

    Abstract:

    Weld defects inside pipelines are the main cause of pipeline leakage and rupture accidents, and X-rays can effectively detect these defects. However, weld defects have problems such as multiple types, small sizes and complex backgrounds, which affect the detection accuracy. To address the current deep learning-based weld defect detection model"s insufficient adaptability to image complex background and lighting changes, and poor detection of small targets. We add the channel attention mechanism and modify the residual block structure on the backbone network of Faster R-CNN network, and adopt the improved model of ROI align to replace the ROI Pooling of traditional Faster R-CNN network. The experimental results show that compared with the original algorithm, the improved Faster R-CNN network model improves the mean Average Precision (mAP) and F1 score by15.82% and 15.82%, respectively, which can meet the requirements of high-precision detection of weld defects, and it has important theoretical significance and good It has important theoretical significance and good prospects for engineering application.

    参考文献
    相似文献
    引证文献
引用本文

陈利琼,梅后金,胡洪宣,等. 基于改进Faster R-CNN的焊缝缺陷检测方法[J]. 科学技术与工程, , ():

复制
分享
文章指标
  • 点击次数:
  • 下载次数:
  • HTML阅读次数:
  • 引用次数:
历史
  • 收稿日期:2024-04-12
  • 最后修改日期:2024-05-24
  • 录用日期:2024-06-05
  • 在线发布日期:
  • 出版日期:
×
亟待确认版面费归属稿件,敬请作者关注