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Advanced Image Stitching Method for Dual-Sensor Inspection.
Shahsavarani, Sara; Lopez, Fernando; Ibarra-Castanedo, Clemente; Maldague, Xavier P V.
Afiliación
  • Shahsavarani S; Computer Vision and Systems Laboratory (CVSL), Department of Electrical and Computer Engineering, Faculty of Science and Engineering, Laval University, Quebec City, QC G1V 0A6, Canada.
  • Lopez F; TORNGATS, 200 Boul. du Parc-Technologique, Quebec City, QC G1P 4S3, Canada.
  • Ibarra-Castanedo C; Computer Vision and Systems Laboratory (CVSL), Department of Electrical and Computer Engineering, Faculty of Science and Engineering, Laval University, Quebec City, QC G1V 0A6, Canada.
  • Maldague XPV; Computer Vision and Systems Laboratory (CVSL), Department of Electrical and Computer Engineering, Faculty of Science and Engineering, Laval University, Quebec City, QC G1V 0A6, Canada.
Sensors (Basel) ; 24(12)2024 Jun 11.
Article en En | MEDLINE | ID: mdl-38931562
ABSTRACT
Efficient image stitching plays a vital role in the Non-Destructive Evaluation (NDE) of infrastructures. An essential challenge in the NDE of infrastructures is precisely visualizing defects within large structures. The existing literature predominantly relies on high-resolution close-distance images to detect surface or subsurface defects. While the automatic detection of all defect types represents a significant advancement, understanding the location and continuity of defects is imperative. It is worth noting that some defects may be too small to capture from a considerable distance. Consequently, multiple image sequences are captured and processed using image stitching techniques. Additionally, visible and infrared data fusion strategies prove essential for acquiring comprehensive information to detect defects across vast structures. Hence, there is a need for an effective image stitching method appropriate for infrared and visible images of structures and industrial assets, facilitating enhanced visualization and automated inspection for structural maintenance. This paper proposes an advanced image stitching method appropriate for dual-sensor inspections. The proposed image stitching technique employs self-supervised feature detection to enhance the quality and quantity of feature detection. Subsequently, a graph neural network is employed for robust feature matching. Ultimately, the proposed method results in image stitching that effectively eliminates perspective distortion in both infrared and visible images, a prerequisite for subsequent multi-modal fusion strategies. Our results substantially enhance the visualization capabilities for infrastructure inspection. Comparative analysis with popular state-of-the-art methods confirms the effectiveness of the proposed approach.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Idioma: En Revista: Sensors (Basel) Año: 2024 Tipo del documento: Article País de afiliación: Canadá Pais de publicación: Suiza
Texto completo
Añadir a Mi BVS
Imprimir
XML
PubMed Links
Buscar en Google

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Idioma: En Revista: Sensors (Basel) Año: 2024 Tipo del documento: Article País de afiliación: Canadá Pais de publicación: Suiza