セグメンテーションDNNにおける不明瞭境界を考慮した損失関数の提案

抄録

This study deals with the task of segmentation of SEM images of fine ceramics sintered bodies by using Deep Neural Network (DNN). In particular, we focus on misclassification caused by the blurriness of grain boundaries(boundaries between particles). Therefore, we utilize the frequency distribution of brightness gradient of grain boundaries and give higher weights to pixels with lower gradient values. Experiments confirmed that the model trained with proposed loss function gave the best prediction results.