The improving performance of high-resolution magnetic instruments has propelled the refined detection of small targets of different shapes. The traditional method is to extract target-related features through some empirical formulas and then classify the targets visually. Such manually designed formulas have limited feature expression ability. Also, the results interpreted by different people may vary. Although machine learning was later applied to the task, which avoided the link of manual judgment, the accuracy and robustness were not strong. In this letter, an end-to-end region-convolutional neural network (R-CNN) is proposed to identify targets with little human intervention. Considering the differences between magnetic signals and natural images in terms of scenario, target, and imaging, improvements to R-CNN meta-architecture are required. Specifically, magnetic tensor gradient (MTG) data with grid cells are transformed into 2-D matrix, which is then enhanced by pseudocolor coding for mapping. Given the self-built dataset, we design a two-stage fine-grained (TSFG) R-CNN to excavate effective deep-level features of targets. PointRend is used here to predict the high-quality edge segmentation of targets. Experiment results show that the proposed method provides a useful way for the detection of multiscale, multishape, and multidepth magnetic targets, even under the case of magnetic field superimposition.

中文翻译：

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使用基于 PointRend 的区域卷积神经网络进行磁性目标检测


高分辨率磁仪器性能的提升推动了对不同形状小目标的精细化检测。传统的方法是通过一些经验公式提取目标相关特征，然后对目标进行直观的分类。这种人工设计的公式特征表达能力有限。而且，不同的人解读的结果可能会有所不同。虽然后来将机器学习应用到该任务中，避免了人工判断的环节，但准确性和鲁棒性并不强。在这封信中，提出了一种端到端区域卷积神经网络（R-CNN）来识别目标，而无需人工干预。考虑到磁信号与自然图像在场景、目标和成像方面的差异，需要对 R-CNN 元架构进行改进。具体来说，将具有网格单元的磁张量梯度（MTG）数据转换为二维矩阵，然后通过伪彩色编码增强该矩阵以进行映射。给定自建数据集，我们设计了一个两阶段细粒度（TSFG）R-CNN来挖掘目标的有效深层特征。这里使用PointRend来预测目标的高质量边缘分割。实验结果表明，即使在磁场叠加的情况下，该方法也为多尺度、多形状、多深度磁性目标的检测提供了一种有用的方法。