Optimizing Wind Turbine Surface Defect Detection: A Rotated Bounding Box Approach

• Published in: 2024 32nd European Signal Processing Conference (EUSIPCO) pp. 631 - 635
• Main Authors: Gohar, Imad, Halimi, Abderrahim, Yew, Weng Kean, See, John
• Format: Conference Proceeding
• Language: English
• Published: European Association for Signal Processing - EURASIP 26.08.2024
• Subjects: Defect detection; Location awareness; oriented bounding boxes; Reliability; Remote sensing; Satellite images; Signal processing; Soft sensors; surface defect detection; UAV remote sensing; Visualization; wind turbine blade; Wind turbines

Detecting surface defects on Wind Turbine Blades (WTBs) from remotely sensed images is a crucial step toward automated visual inspection. Typical object detection algorithms use standard bounding boxes to locate defects on WTBs. However, Oriented Bounding Boxes (OBBs) have been shown in cases of satellite imagery, to provide more precise localization of object regions and actual orientation. Existing WTB datasets do not depict defects using OBBs and this causes the lack of useful orientational information. In this paper, we consider OBBs for WTB surface defect detection through two publicly available datasets, introducing new annotations to the community. Base-lines were constructed on state-of-the-art rotated object detectors, demonstrating considerable promise and known gaps that can be addressed in the future. We present a comprehensive analysis of their performances including ablation study and discussions on the importance of angular disparity between OBBs.