Magnetic flux leakage defect recognition method based on attention feature fusion
Aiming at the problem of low recognition rate and slow detection speed of magnetic flux leakage(MFL)defects,a MFL defect recognition method based on attention feature fusion was proposed.The algorithm was modified on the basis of CenterNet.A lightweight network PP-LCNet was selected as the backbone network,which could simultaneously guarantee low computation and high accuracy,compared with the popular backbone feature extraction network.At the same time,the attention network CBAM was used to positively learn the important information about the low-level features and integrate it with the high-level features.The model could obtain both low-level fine-grained information and high-level semantic information to improve the accuracy of small defect recognition.The experimental results show that the accuracy of as-proposed algorithm is 94.3%and the inference time is 9.6 ms,respectively,when the IOU is greater than 0.5.
万方数据
维普
