Open AccessA multiple strategy for plant species identification using images of leaf texture
Author(s) -
Gerivan Junior,
Janderson Ferreira,
Cristian Millan-Aria,
Ramiro Daniel,
Alberto Cacho Casado,
Bruno Fernandes
Publication year - 2021
Language(s) - English
Resource type - Conference proceedings
DOI - 10.52591/lxai202107245
Subject(s) - robustness (evolution) , feature extraction , artificial intelligence , segmentation , computer science , plant identification , pattern recognition (psychology) , plant species , texture (cosmology) , image texture , identification (biology) , plant taxonomy , fractal , computer vision , image segmentation , mathematics , image (mathematics) , taxonomy (biology) , botany , biology , mathematical analysis , biochemistry , gene , systematics
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. Besides, the detachment of a ceramic plate, exposing the building structure, can still reach people who move around the building. 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 using deep learning to segment these defects. 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.