基于改进SSD网络的管道漏磁缺陷图像识别算法
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摘要
为实现管道漏磁图像的智能化识别,提出一种基于改进SSD网络的管道漏磁图像识别算法。以SSD网络模型为基础框架,加入多孔卷积。利用多孔卷积扩大网络模型的感受野,将低分辨率的高语义信息特征提取出来,从而提高网络对小目标细节特征的学习能力。实验结果表明,提出的算法能自动识别出漏磁数据的环焊缝、螺旋焊缝、缺陷等位置,准确率能达到到92.62%,误检率低于3%,漏检率低于6%,具有更优良的鲁棒性。
In order to realize intelligent detection of pipeline magnetic flux leakage image,a pipeline magnetic flux leakage image detection algorithm based on depth learning is proposed.A dilated convolution is added based on SSD network model.Dilated convolution is used to expand the perception field of the network model and extract low-resolution high-semantic information features,so as to improve the learning ability of the network for small target details.The experimental results show that the proposed algorithm can automatically identify the location of circumferential weld,spiral weld and defect of magnetic flux leakage data.The accuracy can reach 92.62%,the mistake detection rate is less than 3%,the missed detection rate is less than 6%.It has better robustness.
In order to realize intelligent detection of pipeline magnetic flux leakage image,a pipeline magnetic flux leakage image detection algorithm based on depth learning is proposed.A dilated convolution is added based on SSD network model.Dilated convolution is used to expand the perception field of the network model and extract low-resolution high-semantic information features,so as to improve the learning ability of the network for small target details.The experimental results show that the proposed algorithm can automatically identify the location of circumferential weld,spiral weld and defect of magnetic flux leakage data.The accuracy can reach 92.62%,the mistake detection rate is less than 3%,the missed detection rate is less than 6%.It has better robustness.
引文
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