  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.
