论文标题
分割重叠的磨损颗粒，几乎没有标记的数据和不平衡样本
Segmentation overlapping wear particles with few labelled data and imbalance sample
论文作者
论文摘要
Ferrograph图像分割对于获得磨损颗粒的特征具有重要意义。但是，磨损颗粒通常以碎屑链的形式重叠，这对细分磨损碎屑面临挑战。在本研究中提出了重叠的磨损颗粒分割网络（OWPSNET），以分割重叠的碎屑链。提出的深度学习模型包括三个部分：区域分割网络，边缘检测网络和功能精炼模块。区域分割网络是一个改进的U形网络，它用于分离超图像的磨损碎屑形式背景。边缘检测网络用于检测磨损颗粒的边缘。然后，精炼模块结合了低级特征和高级语义特征，以获得最终结果。为了解决样本不平衡问题，我们提出了一个方形骰子损失函数以优化模型。最后，在Ferrograph图像数据集上进行了广泛的实验。结果表明，所提出的模型能够分离重叠的磨损颗粒。此外，提出的正方形骰子损失函数可以改善分割结果，尤其是对于磨损颗粒边缘的分割结果。
Ferrograph image segmentation is of significance for obtaining features of wear particles. However, wear particles are usually overlapped in the form of debris chains, which makes challenges to segment wear debris. An overlapping wear particle segmentation network (OWPSNet) is proposed in this study to segment the overlapped debris chains. The proposed deep learning model includes three parts: a region segmentation network, an edge detection network and a feature refine module. The region segmentation network is an improved U shape network, and it is applied to separate the wear debris form background of ferrograph image. The edge detection network is used to detect the edges of wear particles. Then, the feature refine module combines low-level features and high-level semantic features to obtain the final results. In order to solve the problem of sample imbalance, we proposed a square dice loss function to optimize the model. Finally, extensive experiments have been carried out on a ferrograph image dataset. Results show that the proposed model is capable of separating overlapping wear particles. Moreover, the proposed square dice loss function can improve the segmentation results, especially for the segmentation results of wear particle edge.
