Semantic segmentation of defects in infrastructures through multi-modal images

Sara Shahsavarani; Fernando Lopez; Clemente Ibarra-Castanedo; Xavier Maldague

doi:10.1117/12.3013884

7 June 2024 Semantic segmentation of defects in infrastructures through multi-modal images

Sara Shahsavarani, Fernando Lopez, Clemente Ibarra-Castanedo, Xavier Maldague

Proceedings Volume 13047, Thermosense: Thermal Infrared Applications XLVI; 1304719 (2024) https://doi.org/10.1117/12.3013884
Event: SPIE Defense + Commercial Sensing, 2024, National Harbor, Maryland, United States

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Abstract

The accurate segmentation and detection of defects in infrared and visible images are critical for non-destructive testing applications, however those steps are often excluded by limited annotated training data. This paper presents an innovative approach for the segmentation and detection tasks into a unified framework. The proposed method introduces and tests a novel framework tailored to the domain of infrared and visible imaging. This framework eliminates the need for annotated defect data during training, enabling models to adapt to real-world scenarios where annotations are scarce. To enhance the accuracy of segmentation and detection, it employs super-pixel segmentation, following by texture analysis.

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Sara Shahsavarani, Fernando Lopez, Clemente Ibarra-Castanedo, and Xavier Maldague "Semantic segmentation of defects in infrastructures through multi-modal images", Proc. SPIE 13047, Thermosense: Thermal Infrared Applications XLVI, 1304719 (7 June 2024); https://doi.org/10.1117/12.3013884

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