Abstract
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For the current visual detection methods of wind turbine blade defects,
their detection models are usually excessively large, making them
difficult to achieve a balance between model accuracy and inference
speed. To tackle this problem, this paper introduces a lightweight wind
turbine blade defect detection network, GCB-YOLO, which attempts to
maintain a high detection accuracy and simultaneously achieve a rapid
detection speed. At the beginning, a GhostNet network is employed to
replace a portion of the YOLOv5s backbone network responsible for
feature extraction. This replacement serves to reduce the network’s
parameter size and computational load, thereby achieving compression of
the feature extraction network. Subsequently, a CA (Channel Attention)
mechanism is incorporated into the backbone network, which enhances its
ability to focus on small-sized defects. Finally, the neck network PANet
is substituted with a Bifpn network, bolstering its ability to discern
small-sized defects. A series of validation experiments were conducted
using an image dataset gathered from real wind farms. The result showed
that the GCB-YOLO exhibited a reduction of 46.2% of model parameter
number compared to that of the YOLOv5s. The improved model only has a
7.5MB volume. Hence, in GPU computation mode, the image detection speed
reached 115.3 frames per second. More importantly, the proposed method
achieved an
[email protected] of 94.72%, simplifying the deployment on edge
computing devices and simultaneously meeting the real-time defect
detection requirement with a sustained high level of detection accuracy.