Liu Junwei, Zhang Lihua, Chen Shuguang, Wang Yesong, Wu Binbin, Dong Junjun
Jiangsu University of Science and Technology, Zhengjiang, China.
China Academy of Machinery Ningbo Academy of Intelligent Machine Tool Co. Ltd, Ningbo, China.
PLoS One. 2025 Aug 12;20(8):e0330031. doi: 10.1371/journal.pone.0330031. eCollection 2025.
Oversized rings in wind turbines are regarded as crucial components because they often serve as the main load-bearing and connector structures. Surface defects on these rings can disrupt the normal operation of the entire unit. Detecting surface defects on oversized rings in wind turbine generators (WTGs) is highly challenging due to the huge ring size and small target defects, which will cause the detection process to be very time-consuming and difficult to achieve the expected accuracy. To address this challenge, we propose a new lightweight multiscale high-efficiency detector (LMHD) that balances accuracy and model size. The framework utilizes RepViT as the detection backbone and incorporates a bi-directional feature pyramid network (BiFPN) in the neck to achieve bi-directional feature transfer and aggregation. Additionally, it includes a new lightweight, efficient, multi-scale cross-stage partition module called the Diverse View Group Shuffle Cross Stage Partial Network (DVOV-GSCSPM), which employs a rational architecture and multiscale information fusion to ensure that the overall model is lightweight while maintaining a rich gradient flow. Self-Calibrated Convolutions (SCConv) and Efficient Local Attention (ELA) modules are introduced into the neck network to reduce computational complexity and the number of parameters while ensuring model accuracy. Ultimately, we incorporate the Powerful-IoUv2 loss function to enhance the rate of model convergence and generalization capabilities. The model is experimentally validated on the public dataset NEU-DET, achieving a detection accuracy of 87.0% with 70.4 frames per second (FPS).
风力涡轮机中的超大尺寸环形部件被视为关键组件，因为它们通常充当主要的承重和连接结构。这些环形部件上的表面缺陷会干扰整个机组的正常运行。由于环形部件尺寸巨大且目标缺陷较小，检测风力发电机组（WTG）中超大尺寸环形部件的表面缺陷极具挑战性，这会导致检测过程非常耗时，且难以达到预期的精度。为应对这一挑战，我们提出了一种新的轻量级多尺度高效检测器（LMHD），它在精度和模型大小之间取得了平衡。该框架利用RepViT作为检测主干，并在颈部合并了双向特征金字塔网络（BiFPN）以实现双向特征传递和聚合。此外，它还包括一个名为多样视图组混洗跨阶段部分网络（DVOV - GSCSPM）的新型轻量级、高效、多尺度跨阶段分区模块，该模块采用合理的架构和多尺度信息融合，以确保整体模型在保持丰富梯度流的同时保持轻量级。自校准卷积（SCConv）和高效局部注意力（ELA）模块被引入到颈部网络中，以在确保模型精度的同时降低计算复杂度和参数数量。最终，我们纳入了强大的IoUv2损失函数以提高模型收敛速度和泛化能力。该模型在公共数据集NEU - DET上经过实验验证，实现了87.0%的检测准确率，每秒帧数为70.4帧（FPS）。
