Next Article in Journal
Detecting and Locating Passive Video Forgery Based on Low Computational Complexity Third-Order Tensor Representation
Next Article in Special Issue
Efficient Rank-Based Diffusion Process with Assured Convergence
Previous Article in Journal
Covariate Model of Pixel Vector Intensities of Invasive H. sosnowskyi Plants
Previous Article in Special Issue
The Quantum Nature of Color Perception: Uncertainty Relations for Chromatic Opposition
Article

Image Enhanced Mask R-CNN: A Deep Learning Pipeline with New Evaluation Measures for Wind Turbine Blade Defect Detection and Classification

1
Department of Computer Science, School of Science, Loughborough University, Loughborough LE11 3TT, UK
2
Railston & Co. Ltd., Nottingham NG7 2TU, UK
*
Authors to whom correspondence should be addressed.
Academic Editor: Gonzalo Pajares Martinsanz
Received: 28 January 2021 / Revised: 26 February 2021 / Accepted: 1 March 2021 / Published: 4 March 2021
(This article belongs to the Special Issue 2020 Selected Papers from Journal of Imaging Editorial Board Members)
Demand for wind power has grown, and this has increased wind turbine blade (WTB) inspections and defect repairs. This paper empirically investigates the performance of state-of-the-art deep learning algorithms, namely, YOLOv3, YOLOv4, and Mask R-CNN for detecting and classifying defects by type. The paper proposes new performance evaluation measures suitable for defect detection tasks, and these are: Prediction Box Accuracy, Recognition Rate, and False Label Rate. Experiments were carried out using a dataset, provided by the industrial partner, that contains images from WTB inspections. Three variations of the dataset were constructed using different image augmentation settings. Results of the experiments revealed that on average, across all proposed evaluation measures, Mask R-CNN outperformed all other algorithms when transformation-based augmentations (i.e., rotation and flipping) were applied. In particular, when using the best dataset, the mean Weighted Average (mWA) values (i.e., mWA is the average of the proposed measures) achieved were: Mask R-CNN: 86.74%, YOLOv3: 70.08%, and YOLOv4: 78.28%. The paper also proposes a new defect detection pipeline, called Image Enhanced Mask R-CNN (IE Mask R-CNN), that includes the best combination of image enhancement and augmentation techniques for pre-processing the dataset, and a Mask R-CNN model tuned for the task of WTB defect detection and classification. View Full-Text
Keywords: defect detection; wind turbine blade; deep learning; convolutional neural network; region-based convolutional neural networks; evaluation measure; mask R-CNN; YOLOv3; YOLOv4 defect detection; wind turbine blade; deep learning; convolutional neural network; region-based convolutional neural networks; evaluation measure; mask R-CNN; YOLOv3; YOLOv4
Show Figures

Graphical abstract

MDPI and ACS Style

Zhang, J.; Cosma, G.; Watkins, J. Image Enhanced Mask R-CNN: A Deep Learning Pipeline with New Evaluation Measures for Wind Turbine Blade Defect Detection and Classification. J. Imaging 2021, 7, 46. https://0-doi-org.brum.beds.ac.uk/10.3390/jimaging7030046

AMA Style

Zhang J, Cosma G, Watkins J. Image Enhanced Mask R-CNN: A Deep Learning Pipeline with New Evaluation Measures for Wind Turbine Blade Defect Detection and Classification. Journal of Imaging. 2021; 7(3):46. https://0-doi-org.brum.beds.ac.uk/10.3390/jimaging7030046

Chicago/Turabian Style

Zhang, Jiajun; Cosma, Georgina; Watkins, Jason. 2021. "Image Enhanced Mask R-CNN: A Deep Learning Pipeline with New Evaluation Measures for Wind Turbine Blade Defect Detection and Classification" J. Imaging 7, no. 3: 46. https://0-doi-org.brum.beds.ac.uk/10.3390/jimaging7030046

Find Other Styles
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Search more from Scilit
 
Search
Back to TopTop