A real-time material breakage detection for offshore wind turbines based on improved neural network algorithm

• URL: http://arxiv.org/abs/2307.13765v1
• Date: Tue, 25 Jul 2023 18:50:05 GMT
• Title: A real-time material breakage detection for offshore wind turbines based on improved neural network algorithm
• Authors: Yantong Liu
• Abstract summary: This study introduces a novel approach leveraging an advanced version of the YOLOv8 object detection model. We employ a dataset of 5,432 images from the Saemangeum offshore wind farm and a publicly available dataset. The findings reveal a substantial enhancement in defect detection stability, marking a significant stride towards efficient turbine maintenance.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The integrity of offshore wind turbines, pivotal for sustainable energy generation, is often compromised by surface material defects. Despite the availability of various detection techniques, limitations persist regarding cost-effectiveness, efficiency, and applicability. Addressing these shortcomings, this study introduces a novel approach leveraging an advanced version of the YOLOv8 object detection model, supplemented with a Convolutional Block Attention Module (CBAM) for improved feature recognition. The optimized loss function further refines the learning process. Employing a dataset of 5,432 images from the Saemangeum offshore wind farm and a publicly available dataset, our method underwent rigorous testing. The findings reveal a substantial enhancement in defect detection stability, marking a significant stride towards efficient turbine maintenance. This study's contributions illuminate the path for future research, potentially revolutionizing sustainable energy practices.

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