Abstract
This thesis presents a semantic segmentation method based on fully-convolutional network (FCN). We focus on increasing mean-class accuracy by adding other steps that help FCN to find more small objects: i) modulating the dropout rates, ii) combining multiple loss functions, and iii) expanding small object areas. Our approach shows that the above steps can significantly increase mean-class accuracy without sacrifice too much per-pixel accuracy. We also provide experimental observations on the relationship between the area-expanding method and the CNN model. Finally, we discuss how to improve the workflow and what we have learned from the experiments of training with multi-loss functions.