The abnormal identification of square-hole lock loosening based on manual mode has the problems of low maintenance efficiency and difficult to ensure the quality of maintenance.In order to improve the maintenance effi-ciency and accuracy of square-hole lock looseness,this paper propose a Siamese residual multi-scale feature fusion network to identify square-hole lock looseness.Aiming at the problem that the multi-scale features are not fully uti-lized in the Siamese residual network,a Feature Fusion Module(FFM)is designed to adaptively fuse the features at different scales.In addition,this paper proposed an augmented algorithm to simulate the square hole lock loose fault,which solves the problem of the small number of real loose data.The experimental results on the test set show that the proposed augmentation algorithm can significantly improve the recognition accuracy of the model,and the F-score was improved.Moreover,compared with the Siamese residual network,the proposed Siamese residual multi-scale fea-ture fusion network has higher recognition accuracy,and the maximum recognition accuracy improvement gain reaches 2.66%on the test set with different loosening angles.
Passenger Car ApronSquare-hole Lock LooseSiamese NetworkResidual NetworkDeep Learning