Research On The Fracture State Identification Of Fastener Clips Based On Deep Learning

The fastener system is an important part of the track structure,and its working state has a direct impact on the stability of the track structure.This thesis studies the image acquisition and pre-processing,the pixel-level segmentation of fastener clips and the identification of fastener status.An identification system of subway fastener fracture state is thus established.The main contents are as follows:（1）Image pre-processing of subway fastener.Three filtering algorithms,namely the mean,Gaussian and median,are compared and analyzed,and 5×5 median filtering template is selected for noise reduction.The contrast between the image fastener area and background is improved using histogram equalization and Laplacian sharpening to weaken the influences of environment on the images.The negative samples of fastener images are expanded using various data enhancement methods to build the sample library of subway fastener images,including2000 normal fastener images,400 front arch fracture fastener images and400 rear arch fracture fastener images.（2）The pixel-level segmentation of subway fastener clips is realized based on the Deep Lab v3+semantic segmentation model.In the semantic segmentation model experiments,the loss convergence speed and convergence value of the training set and the MIo U of the validation set using Deep Lab v3+outperform those of U-Net model.The robustness experiments demonstrate that the Deep Lab v3+semantic segmentation model has high robustness to images with discerning brightness range-25+75 and Gaussian blurσ5.（3）Based on the original image of the fastener and the semantically segmented image,the recognition performance of three deep learning classification models,namely the Mobile Net V2,Shuffle Net V2 and Res Net50,is compared and analyzed.For the original image,Res Net50 is the optimal recognition model and can identify the normal fasteners and the rear arch fractured fasteners better.However,there is a problem of misjudging the front arch fractured fasteners as normal fasteners.For semantically segmented images,the recognition effect of each model is better than that of the original image.It is concluded that the recognition results using the Mobile Net V2 model are better than that of other models using four evaluation indicators,namely the accuracy,precision,recall and F₂ score.The model thus can effectively identify three damage types,namely the normal fasteners,front arch fracture fasteners and rear arch fracture fasteners.（4）A recognition system of subway fastener fracture state based on deep learning is built,including five modules of fastener image acquisition,image pre-processing,fastener clips pixel-level segmentation,fastener state recognition and human-computer interaction.It thus realizes the recognition system integration of subway fastener fracture state.