Skip to yearly menu bar Skip to main content


Poster
in
Affinity Workshop: LatinX in AI (LXAI) Research at ICML 2021

Ceramic Cracks Segmentation with Deep Learning

Gerivan Junior · Janderson Ferreira · Cristian Millán · Ramiro Ruiz · Alberto Junior · Bruno Fernandes

Keywords: [ Multitask, Transfer, and Meta Learning ] [ Algorithms ]


Abstract:

Cracks are pathologies whose appearance in ceramic tiles can cause various types of scratches due to the coating system losing water tightness and impermeability functions. Manual inspection is the most common method for this problem. However, it depends on the knowledge and experience of those who perform the analysis and demands a long time to map the entire area and high cost. These inspections require special equipment when they are at high altitudes, and the integrity of the inspector is at risk. Thus, there exists a need for automated optical inspection to find faults in ceramic tiles. This work focuses on the segmentation of cracks in ceramic images. We propose an architecture for segmenting cracks in facades with Deep Learning that includes a pre-processing step. We also propose the Ceramic Crack Database, a set of images to segment defects in ceramic tiles. The results show that the proposed architecture for ceramic crack segmentation achieves promising performance.