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

Author:

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

Publisher

Springer Nature Switzerland

Reference25 articles.

1. ORE Catapult and the Whitmarsh Supply Chain Review Team. Offshore Wind Industry Prospectus (2018). https://cdn.ymaws.com/www.renewableuk.com/resource/resmgr/publications/catapult_prospectus_final.pdf

2. Mishra, M.: Convolutional Neural Networks, Explained Towards Data Science (2020). https://towardsdatascience.com/convolutional-neural-networks-explained-9cc5188c4939

3. Foster, A., Best, O., Gianni, M., Khan, A., Collins, K., Sharma, S. (Eds.): Drone footage wind turbine surface damage detection. In: 2022 IEEE 14th Image, Video, and Multidimensional Signal Processing Workshop (IVMSP). IEEE (2022)

4. Ultralytics. yolov5 (2020). https://github.com/ultralytics/yolov5

5. Wang, C.-Y., Bochkovskiy, A., Liao, H.-Y.M.: YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors (2022). arXiv:220702696

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