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首页> 外文期刊>IEEE transactions on industrial informatics >Pipeline Magnetic Flux Leakage Image Detection Algorithm Based on Multiscale SSD Network
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Pipeline Magnetic Flux Leakage Image Detection Algorithm Based on Multiscale SSD Network

机译:基于MultiScale SSD网络的流水线磁通漏图像检测算法

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摘要

In order to solve the problem of low detection accuracy of small targets in the SSD detection algorithm, a pipeline magnetic flux leakage image detection algorithm based on multiscale SSD network is proposed in this paper. The dilated convolution and attention residual module are introduced into the SSD algorithm to fuse the low-resolution high-semantic information feature map with the high-resolution low-semantic information feature map so as to improve the resolution of the low-resolution feature map and provide detailed features for small targets. Finally, the target location and category are obtained by regression algorithm. 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. Compared with the original SSD algorithm framework, the improved algorithm has higher detection accuracy, 97.62, 18.01 lower false detection rate, 18.36 lower false detection rate, better robustness, and obvious effect on small target detection.
机译:为了解决SSD检测算法中小目标的低检测精度的问题,本文提出了一种基于多尺度SSD网络的流水线磁通漏图像检测算法。扩张的卷积和注意力剩余模块被引入SSD算法,以融合低分辨率低语义信息特征映射,以提高低分辨率特征映射的分辨率和为小目标提供详细的功能。最后,通过回归算法获得目标位置和类别。实验结果表明,该算法可以自动识别圆周焊接,螺旋焊缝和磁通泄漏数据缺陷的位置。与原始SSD算法框架相比,改进的算法检测精度较高,97.62,18.01较低的假检测率,18.36较低的假检测率,更好的鲁棒性和对小目标检测的显而易见的影响。

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