Defects recognition of strain clamps based on attentional enhancement mechanism

The crimping quality of tension clamp in overhead transmission line is directly related to the safe and stable operation of transmission line. existing detection methods are low efficiency or availability and developing a timely and effective defect detecting method for strain clamp is a top priority. An X-ray image classification algorithm based on the attentional enhancement mechanism is proposed to identify the defects of different types of strain clamps. DenseNet structure is used as the backbone network to multiplex the X-ray image features, SENet channel is used to enhance the attention to the defect site, and PSANet is used to replace the 3×3 convolution layer in DenseBlock in DenseNet to highlight the features of defects at different scales. Research shows that the average recognition accuracy of the proposed algorithm is 88.1%, which is better than the existing methods.