Wind Turbine Surface Damage Detection Using YOLOv7 with Slicing Aided Hyper Inference (SAHI)

Oscar Best*, Asiya Khan, Mario Gianni, Sanjay Sharma, Keri Collins

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference proceedings published in a bookpeer-review

Abstract

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.

Original languageEnglish
Title of host publicationIntelligent Systems and Applications - Proceedings of the 2023 Intelligent Systems Conference IntelliSys Volume 2
EditorsKohei Arai
PublisherSpringer Science and Business Media Deutschland GmbH
Pages564-576
Number of pages13
ISBN (Print)9783031477232
DOIs
Publication statusPublished - 2024
EventIntelligent Systems Conference, IntelliSys 2023 - Amsterdam, Netherlands
Duration: 7 Sept 20238 Sept 2023

Publication series

NameLecture Notes in Networks and Systems
Volume823 LNNS
ISSN (Print)2367-3370
ISSN (Electronic)2367-3389

Conference

ConferenceIntelligent Systems Conference, IntelliSys 2023
Country/TerritoryNetherlands
CityAmsterdam
Period7/09/238/09/23

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Signal Processing
  • Computer Networks and Communications

Keywords

  • Computer Vision
  • Machine Learning
  • Wind Turbine Inspection

Fingerprint

Dive into the research topics of 'Wind Turbine Surface Damage Detection Using YOLOv7 with Slicing Aided Hyper Inference (SAHI)'. Together they form a unique fingerprint.

Cite this