高级检索
王睿, 高少泽, 刘卫朋, 王刚. 一种轻量级高效X射线焊缝图像缺陷检测方法[J]. 焊接学报. DOI: 10.12073/j.hjxb.20230630003
引用本文: 王睿, 高少泽, 刘卫朋, 王刚. 一种轻量级高效X射线焊缝图像缺陷检测方法[J]. 焊接学报. DOI: 10.12073/j.hjxb.20230630003
WANG Rui, GAO Shaoze, LIU Weipeng, WANG Gang. A lightweight and efficient x-ray weld image defect detection method[J]. TRANSACTIONS OF THE CHINA WELDING INSTITUTION. DOI: 10.12073/j.hjxb.20230630003
Citation: WANG Rui, GAO Shaoze, LIU Weipeng, WANG Gang. A lightweight and efficient x-ray weld image defect detection method[J]. TRANSACTIONS OF THE CHINA WELDING INSTITUTION. DOI: 10.12073/j.hjxb.20230630003

一种轻量级高效X射线焊缝图像缺陷检测方法

A lightweight and efficient x-ray weld image defect detection method

  • 摘要: 针对当前深度学习模型在焊缝缺陷检测工作中成本高、速度慢、不易在终端部署应用等问题,提出一种效果良好的轻量级高效模型(lightweight and high-precision optimized detection model,LHODM). 首先,搭建新型主干网络并设计超轻量卷积ULConv作为网络基础卷积,降低模型深度,用Depthwise操作生成更多有效冗余特征映射,减少参数与运算量. 其次,设计高效轻量级专注模块ELCC,在响应模型轻量化前提下,考虑焊缝缺陷分布特性与成像规律,从水平和垂直两个方向捕获孤立区域关系,结合轻量级上采样算子CARAFE,使模型特征重组时具有更大的感受野,更有效地利用环境周围信息,弥补轻量化造成的精度损失. 最后,为提高收敛速度和损失函数效率,设计OS-IoU损失函数,考虑期望回归向量之间的夹角,重新定义惩罚项及相关性,强化距离损失和形状损失关注程度. 结果表明,LHODM模型检测准确率和检测速度达到91.62%和63.47帧/s,模型内存成本仅为3.99 M,有效解决了焊缝缺陷检测工作中成本高和速度慢的问题.

     

    Abstract: In response to the high cost, slow speed, and difficulty in deploying deep learning models in weld defect detection, this paper proposes a lightweight and high precision optimized detection model (LHODM) with good performance. Firstly, build a new backbone network and design an ultra lightweight convolutional ULConv as the network foundation convolution to reduce model depth, generate more effective redundant feature maps using Depthwise operations, and reduce parameters and computational complexity. Secondly, design an efficient and lightweight dedicated module ELCC, which takes into account the distribution characteristics and imaging rules of weld defects under the premise of lightweight response model. Capture isolated area relationships from both horizontal and vertical directions, and combine with the lightweight upsampling operator CARAFE to make the model feature recombination have a larger receptive field, more effectively utilize environmental information, and compensate for the accuracy loss caused by lightweight. Finally, to improve convergence speed and loss function efficiency, an OS-IoU loss function is designed, taking into account the angle between the expected regression vectors, redefining the penalty term and correlation, and strengthening the attention to distance loss and shape loss. The results showed that the LHODM model achieved detection accuracy and speed of 91.62% and 63.47 frames per second, with a model memory cost of only 3.99 million, effectively solving the problems of high cost and slow speed in weld defect detection work.

     

/

返回文章
返回