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SOD-YOLO: A Small Target Defect Detection Algorithm for Wind Turbine Blades Based on Improved YOLOv5
Advanced Theory and Simulations ( IF 2.9 ) Pub Date : 2022-04-26 , DOI: 10.1002/adts.202100631 Rui Zhang 1 , Chuanbo Wen 1
Advanced Theory and Simulations ( IF 2.9 ) Pub Date : 2022-04-26 , DOI: 10.1002/adts.202100631 Rui Zhang 1 , Chuanbo Wen 1
Affiliation
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Early and effective detection of wind turbine blade (WTB) surface defects can avoid complex and expensive repair problems and serious safety hazards. The traditional target detection methods have the problems of insufficient detection capability, long model inference time and low recognition accuracy for small targets and long strip defects in WTB datasets. This paper proposes a high-precision model SOD-YOLO for WTB surface defect detection based on UAVs image analysis of YOLOv5. First, the WTB images are preprocessed by foreground segmentation and Hough transform to build the WTB defect dataset. Then, a micro-scale detection layer is added to the original YOLOv5, and the K-means algorithm is used to re-cluster anchors and add the CBAM attention mechanism to each feature fusion layer to reduce the loss of feature information for small target defects and other defects. In addition, to improve the detection efficiency, the channel pruning algorithm is used to reduce the model size. The experimental results show that the average accuracy (mAP) of the SOD-YOLO algorithm on the WTB dataset reaches 95.1%, which is 7.82% better than YOLOv5, and the FPS is 28.3% better. Therefore, SOD-YOLO is able to detect small target defects and other defects quickly and effectively.
中文翻译：
SOD-YOLO：一种基于改进YOLOv5的风电叶片小目标缺陷检测算法
早期有效地检测风力涡轮机叶片 (WTB) 表面缺陷可以避免复杂且昂贵的维修问题和严重的安全隐患。传统的目标检测方法存在检测能力不足、模型推理时间长、对WTB数据集中小目标和长条形缺陷识别准确率低等问题。本文基于YOLOv5的无人机图像分析，提出了一种高精度的WTB表面缺陷检测模型SOD-YOLO。首先，WTB图像通过前景分割和霍夫变换进行预处理，构建WTB缺陷数据集。然后，在原有的YOLOv5上增加了一个微尺度的检测层，并采用K-means算法对anchor进行重新聚类，并在每个特征融合层中加入CBAM注意力机制，以减少针对小目标缺陷等缺陷的特征信息丢失。此外，为提高检测效率，采用通道剪枝算法减小模型尺寸。实验结果表明，SOD-YOLO算法在WTB数据集上的平均准确率（mAP）达到95.1%，比YOLOv5提高了7.82%，FPS提高了28.3%。因此，SOD-YOLO 能够快速有效地检测出小目标缺陷和其他缺陷。实验结果表明，SOD-YOLO算法在WTB数据集上的平均准确率（mAP）达到95.1%，比YOLOv5提高了7.82%，FPS提高了28.3%。因此，SOD-YOLO 能够快速有效地检测出小目标缺陷和其他缺陷。实验结果表明，SOD-YOLO算法在WTB数据集上的平均准确率（mAP）达到95.1%，比YOLOv5提高了7.82%，FPS提高了28.3%。因此，SOD-YOLO 能够快速有效地检测出小目标缺陷和其他缺陷。
更新日期：2022-04-26
中文翻译：
SOD-YOLO：一种基于改进YOLOv5的风电叶片小目标缺陷检测算法
早期有效地检测风力涡轮机叶片 (WTB) 表面缺陷可以避免复杂且昂贵的维修问题和严重的安全隐患。传统的目标检测方法存在检测能力不足、模型推理时间长、对WTB数据集中小目标和长条形缺陷识别准确率低等问题。本文基于YOLOv5的无人机图像分析，提出了一种高精度的WTB表面缺陷检测模型SOD-YOLO。首先，WTB图像通过前景分割和霍夫变换进行预处理，构建WTB缺陷数据集。然后，在原有的YOLOv5上增加了一个微尺度的检测层，并采用K-means算法对anchor进行重新聚类，并在每个特征融合层中加入CBAM注意力机制，以减少针对小目标缺陷等缺陷的特征信息丢失。此外，为提高检测效率，采用通道剪枝算法减小模型尺寸。实验结果表明，SOD-YOLO算法在WTB数据集上的平均准确率（mAP）达到95.1%，比YOLOv5提高了7.82%，FPS提高了28.3%。因此，SOD-YOLO 能够快速有效地检测出小目标缺陷和其他缺陷。实验结果表明，SOD-YOLO算法在WTB数据集上的平均准确率（mAP）达到95.1%，比YOLOv5提高了7.82%，FPS提高了28.3%。因此，SOD-YOLO 能够快速有效地检测出小目标缺陷和其他缺陷。实验结果表明，SOD-YOLO算法在WTB数据集上的平均准确率（mAP）达到95.1%，比YOLOv5提高了7.82%，FPS提高了28.3%。因此，SOD-YOLO 能够快速有效地检测出小目标缺陷和其他缺陷。
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