基于Mask R-CNN的铁谱磨粒智能分割与识别

针对铁谱图像因背景复杂、尺寸分布广、颗粒重叠等导致难以精确分割与识别的问题,以相似度高的疲劳剥块、严重滑动磨粒、层状磨粒共3种异常磨粒作为研究对象,提出基于深度神经网络模型Mask R-CNN的对多目标铁谱磨粒进行智能分割与识别的方法,并对特征提取层分别选用深度不同的残差网络ResNet50和ResNet101进行对比试验。实验结果表明,基于迁移学习方法的Mask R-CNN+ResNet101模型能够在复杂背景下对多目标、多类型、多尺寸的相似磨粒进行有效分割与识别,测试集的平均精度高达76.2%,模型具有较好的泛化能力。

Ferrographic Wear Debris Intelligent Segmentation and Recognition Based on Mask R-CNN

It is difficult to segment and recognize ferrography image precisely due to the complex background,wide size distribution and overlapping debris. An intelligent multi-target wear debris segmentation and recognition method based on the deep neural network model Mask R-CNN was propose to study three kinds of abnormal abrasive,including fatigue spall,severe sliding debris,laminar debris. For feature extraction layer,residual network ResNet50 and ResNet101 with dif- ferent depths were selected for comparative test. The experimental results show that Mask R-CNN+ResNet101 can effec- tively segment and identify ferrographic wear debris of multiple targets,types and sizes under complex background. The average precision of the test set is as high as 76.2%,and the model has good generalization ability.