Block pavements are widely used in various infrastructures, offering durability and aesthetic appeal. However, assessing their condition through manual methods is resource-intensive and subjective. This study proposes a deep learning approach using the Hybrid TransUNet model to enhance the accuracy and efficiency of detecting block pavement distresses. A dataset of over 10,000 images was used to train and test binary and multiclass segmentation models, significantly improving detection accuracy. The results show that the Hybrid TransUNet model outperforms other models, though challenges in detecting certain distress types like cracks persist.

• 에스킨더(중앙대학교 스마트시티학과 석사과정) | Eskndir GetachewDenu
• 임영인(한국공항공사 항공산업연구원 기술연구부 임영인 선임연구원) | Lim Young In
• 조윤호(중앙대학교 사회기반시스템공학부 교수, 공학박사) | Cho Yoon Ho