TY - GEN
T1 - Wind Turbine Surface Damage Detection Using YOLOv7 with Slicing Aided Hyper Inference (SAHI)
AU - Best, Oscar
AU - Khan, Asiya
AU - Gianni, Mario
AU - Sharma, Sanjay
AU - Collins, Keri
N1 - Publisher Copyright:
© The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.
PY - 2024
Y1 - 2024
N2 - This paper expands on the publicly available dataset of wind turbine surface damage and uses this dataset to finetune all P5 models for both YOLOv5 and YOLOv7 object detection frameworks. YOLOv7 outperformed YOLOv5, with the YOLOv7x model achieving the highest recall score and best F1-confidence. This model was therefore selected for inference on both images and video of wind turbines. Slicing Aided Hyper Inference (SAHI) has also been used to improve detection capability for smaller instances of damage. The model was further evaluated on a dataset collected from a scaled model of a wind turbine, with hand drawn damages. Lastly, this dataset was used for inference using SAHI, which showed slight improvement for detecting damage instances. More accurate results were observed when evaluating the model on real damage examples compared to simulated damage.
AB - This paper expands on the publicly available dataset of wind turbine surface damage and uses this dataset to finetune all P5 models for both YOLOv5 and YOLOv7 object detection frameworks. YOLOv7 outperformed YOLOv5, with the YOLOv7x model achieving the highest recall score and best F1-confidence. This model was therefore selected for inference on both images and video of wind turbines. Slicing Aided Hyper Inference (SAHI) has also been used to improve detection capability for smaller instances of damage. The model was further evaluated on a dataset collected from a scaled model of a wind turbine, with hand drawn damages. Lastly, this dataset was used for inference using SAHI, which showed slight improvement for detecting damage instances. More accurate results were observed when evaluating the model on real damage examples compared to simulated damage.
KW - Computer Vision
KW - Machine Learning
KW - Wind Turbine Inspection
UR - http://www.scopus.com/inward/record.url?scp=85192155448&partnerID=8YFLogxK
U2 - 10.1007/978-3-031-47724-9_37
DO - 10.1007/978-3-031-47724-9_37
M3 - Conference proceedings published in a book
AN - SCOPUS:85192155448
SN - 9783031477232
T3 - Lecture Notes in Networks and Systems
SP - 564
EP - 576
BT - Intelligent Systems and Applications - Proceedings of the 2023 Intelligent Systems Conference IntelliSys Volume 2
A2 - Arai, Kohei
PB - Springer Science and Business Media Deutschland GmbH
T2 - Intelligent Systems Conference, IntelliSys 2023
Y2 - 7 September 2023 through 8 September 2023
ER -