[1F4-GS-10] AI application: vision analysis

[1F4-GS-10-04] Improving the accuracy of crack segmentation of revetment using Focal Tversky Loss

〇Yukino Tsuzuki¹, Ryuto Yoshida¹, Junichi Okubo¹, Junichiro Fujii¹, Takayoshi Yamashita² (1. Yachiyo Engineering Co., Ltd., 2. Chubu University)

Keywords:Deep Learnig, Maintenance, Semantic Segmentation, Focal Tversky Loss, Imbalanced Data

A technique for detecting cracks in river concrete revetments using image segmentation technology is being studied. However, the area of the crack to be classified is very small compared to the background. Models trained on such imbalanced data are known to be unstable in prediction accuracy due to optimization that is more influenced by classes with more data than classes with fewer data. Therefore, in order to improve the crack detection accuracy, this paper tries to adopt Focal Tversky Loss, which is a loss function robust to imbalanced data. The model using Focal Tversky Loss showed higher crack detection accuracy than the commonly used Binary Cross Entropy and Dice Loss. In addition, by introducing an attention mechanism into the segmentation model, a visualized image representing a judgment reason as to which part of the image was focused for segmentation was generated.