基于深度学习与图像配准的螺栓松动检测

Bolt looseness detection based on deep learning and image registration

针对风力发电机长时间发生重复振动可能会引起自身螺栓松动,影响风力发电机的正常运作的问题,提出了一种基于深度学习与图像配准的螺栓松动检测方法,避免了人工干预。对YOLOv5s进行了改进,使用ShuffleNetV2作为Backbone,在不降低准确率的情况下检测速度提升了69.8%,实现了对图像中包含待检测螺栓区域的定位。将待检测图像和模板图像采用基于强度的方法进行配准,并通过特征增强的方式对配准后的灰度图进行处理,从而定位出松动螺栓。实验结果表明,当螺栓发生5°以上旋转时该方法能准确检测出螺栓松动。

Aiming at the problem that the repeated vibration of a wind turbine for a long time may cause its own bolt loosening and affect its normal operation,this paper proposes a bolt loosening detection method based on deep learning and image registration to avoid manual intervention.YOLOv5s is improved,and ShuffleNetV2 is used as Backbone to realize the location of the bolt area to be detected in the image,in which process the detection speed increases by 69.8% without reducing the accuracy.After that,the to-be-detected image and the template image are registered through an intensity-based method,and the registered grayscale images are processed by means of feature enhancement,thereby locating the loose bolts.According to the experimental results,the method can accurately detect the looseness of the bolt when the bolt rotates more than 5°.