Identification of Martensite Bands in Dual‐Phase Steels: A Deep Learning Object Detection Approach Using Faster Region‐Based‐Convolutional Neural Network

Martensite banding in dual‐phase steels is an important research topic in the field of materials design, since it affects the local damage properties of the material largely. Therefore, it is necessary to quantify the amount and the geometrical details of the bands in a specific microstructure, for example, for simulative approaches. A convolutional neural network is trained on manually labeled scanning electron microscopy images of DP800 steel and a subsequent effort is made to transfer these results to statistical quantities for the generation of representative volume elements (RVE). As exact geometric definitions of martensite bands in 2D are difficult, the influence of different band definitions is investigated. The result of the training shows good prediction accuracy but is strongly dependent on the chosen band definition and the underlying human bias from the labeling process. A statistical analysis using cross‐validation shows that reliable results can already be achieved with only small datasets of around 50–100 training images due to the transfer learning approach. This is an important outcome as it eliminates the need to generate a large dataset which can only be obtained from time‐consuming microscopy work and manual labeling of the images. Herein, the detection of martensite bands in scanning electron microscopy images of DP800, using a Faster region‐based convolutional network, is studied. The bands are removed according to different band definitions. In general, the algorithm produces good results on restricted data. Subsequently, an approach is presented which directly converts the predictions into statistical quantities for the generation of representative volume elements.