MDD-Net: A novel defect detection model of material microscope image

The defects in the microscopic image of the material have an important influence on the macroscopic properties of the material. Statistics of the number and size of defects in material microscopic images is an essential task to quantitatively study the relationship between micro-structure and macroscopic properties of materials. This task is repetitive and onerous for researchers. Replacing the manpower in this task with computers is helpful to reduce the burden of researchers and beneficial to the development of material science. In this work, a novel defect detection model of material microscope image is proposed, which is called Material Defect Detection Network(MDD-Net). This paper introduced dilated convolution and tailored the Resnet aiming at the small and medium objects dominating problem, introduced deformable convolution aiming at the irregular shape problem, and applied focal loss solving imbalance between foreground and background. The correctness and feasibility of the above ideas are verified through experiments. With the ideas, MDD-Net achieve 17.17% higher precision and 7.93% higher recall compared with baseline.

[1]  Léon Bottou,et al.  Large-Scale Machine Learning with Stochastic Gradient Descent , 2010, COMPSTAT.

[2]  Stephen Lin,et al.  Deformable ConvNets V2: More Deformable, Better Results , 2018, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

[3]  Jian Sun,et al.  Deep Residual Learning for Image Recognition , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[4]  Hai Su,et al.  Beyond Classification: Structured Regression for Robust Cell Detection Using Convolutional Neural Network , 2015, MICCAI.

[5]  Jian Sun,et al.  Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition , 2014, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[6]  Trevor Darrell,et al.  Rich Feature Hierarchies for Accurate Object Detection and Semantic Segmentation , 2013, 2014 IEEE Conference on Computer Vision and Pattern Recognition.

[7]  Lin Yang,et al.  A Novel Cell Detection Method Using Deep Convolutional Neural Network and Maximum-Weight Independent Set , 2015, MICCAI.

[8]  Iasonas Kokkinos,et al.  DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs , 2016, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[9]  Nasir M. Rajpoot,et al.  Simultaneous Cell Detection and Classification with an Asymmetric Deep Autoencoder in Bone Marrow Histology Images , 2017, MIUA.

[10]  Vladlen Koltun,et al.  Multi-Scale Context Aggregation by Dilated Convolutions , 2015, ICLR.

[11]  Kai Chen,et al.  MMDetection: Open MMLab Detection Toolbox and Benchmark , 2019, ArXiv.

[12]  Kaiming He,et al.  Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks , 2015, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[13]  Yi Li,et al.  Deformable Convolutional Networks , 2017, 2017 IEEE International Conference on Computer Vision (ICCV).

[14]  Michael S. Bernstein,et al.  ImageNet Large Scale Visual Recognition Challenge , 2014, International Journal of Computer Vision.

[15]  Ross B. Girshick,et al.  Fast R-CNN , 2015, 1504.08083.

[16]  Kaiming He,et al.  Focal Loss for Dense Object Detection , 2017, 2017 IEEE International Conference on Computer Vision (ICCV).

[17]  Pietro Perona,et al.  Microsoft COCO: Common Objects in Context , 2014, ECCV.

[18]  Kaiming He,et al.  Feature Pyramid Networks for Object Detection , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[19]  Frank Hutter,et al.  SGDR: Stochastic Gradient Descent with Warm Restarts , 2016, ICLR.

[20]  Hai Su,et al.  Efficient and robust cell detection: A structured regression approach , 2018, Medical Image Anal..

[21]  Luc Van Gool,et al.  The Pascal Visual Object Classes (VOC) Challenge , 2010, International Journal of Computer Vision.

[22]  Junzhou Huang,et al.  Subtype Cell Detection with an Accelerated Deep Convolution Neural Network , 2016, MICCAI.