To guarantee the safety of railway operations,the swift detection of rail surface defects becomes imperative.Traditional methods of manual inspection and conventional nondestructive testing prove inefficient,especiall...To guarantee the safety of railway operations,the swift detection of rail surface defects becomes imperative.Traditional methods of manual inspection and conventional nondestructive testing prove inefficient,especially when scaling to extensive railway networks.Moreover,the unpredictable and intricate nature of defect edge shapes further complicates detection efforts.Addressing these challenges,this paper introduces an enhanced Unified Perceptual Parsing for Scene Understanding Network(UPerNet)tailored for rail surface defect detection.Notably,the Swin Transformer Tiny version(Swin-T)network,underpinned by the Transformer architecture,is employed for adept feature extraction.This approach capitalizes on the global information present in the image and sidesteps the issue of inductive preference.The model’s efficiency is further amplified by the windowbased self-attention,which minimizes the model’s parameter count.We implement the cross-GPU synchronized batch normalization(SyncBN)for gradient optimization and integrate the Lovász-hinge loss function to leverage pixel dependency relationships.Experimental evaluations underscore the efficacy of our improved UPerNet,with results demonstrating Pixel Accuracy(PA)scores of 91.39%and 93.35%,Intersection over Union(IoU)values of 83.69%and 87.58%,Dice Coefficients of 91.12%and 93.38%,and Precision metrics of 90.85%and 93.41%across two distinct datasets.An increment in detection accuracy was discernible.For further practical applicability,we deploy semantic segmentation of rail surface defects,leveraging connected component processing techniques to distinguish varied defects within the same frame.By computing the actual defect length and area,our deep learning methodology presents results that offer intuitive insights for railway maintenance professionals.展开更多
Heavy-haul railways play a vital role in freight transportation,and the health of the rails directly impacts the safety and efficiency of railway operations.The heavy axle loads and long train compositions of heavy-ha...Heavy-haul railways play a vital role in freight transportation,and the health of the rails directly impacts the safety and efficiency of railway operations.The heavy axle loads and long train compositions of heavy-haul trains make the rail surface susceptible to damage such as rail corrugation,spalling and abrasion,threatening operational safety.To address the issue,this paper proposes a multi-source data fusion method for identifying rail surface defects on heavy-haul railways.First,complete ensemble empirical mode decomposition with adaptive noise is used to decompose vibration signals and extract multi-dimensional vibration features.Next,dynamic time warping is applied to align rail profile data and extract key geometric features.Then,the vibration features and profile features are fused using Relief-F to select the most discriminative features.Finally,a support vector machine is utilized for defect identification.Experiment results show that the proposed method achieves high accuracy in identifying rail surface defects,with an accuracy of 96.4%.展开更多
基金supported in part by the National Natural Science Foundation of China(Grant No.62066024)Gansu Province Higher Education Industry Support Plan(2021CYZC34)Lanzhou Talent Innovation and Entrepreneurship Project(2021-RC-27,2021-RC-45).
文摘To guarantee the safety of railway operations,the swift detection of rail surface defects becomes imperative.Traditional methods of manual inspection and conventional nondestructive testing prove inefficient,especially when scaling to extensive railway networks.Moreover,the unpredictable and intricate nature of defect edge shapes further complicates detection efforts.Addressing these challenges,this paper introduces an enhanced Unified Perceptual Parsing for Scene Understanding Network(UPerNet)tailored for rail surface defect detection.Notably,the Swin Transformer Tiny version(Swin-T)network,underpinned by the Transformer architecture,is employed for adept feature extraction.This approach capitalizes on the global information present in the image and sidesteps the issue of inductive preference.The model’s efficiency is further amplified by the windowbased self-attention,which minimizes the model’s parameter count.We implement the cross-GPU synchronized batch normalization(SyncBN)for gradient optimization and integrate the Lovász-hinge loss function to leverage pixel dependency relationships.Experimental evaluations underscore the efficacy of our improved UPerNet,with results demonstrating Pixel Accuracy(PA)scores of 91.39%and 93.35%,Intersection over Union(IoU)values of 83.69%and 87.58%,Dice Coefficients of 91.12%and 93.38%,and Precision metrics of 90.85%and 93.41%across two distinct datasets.An increment in detection accuracy was discernible.For further practical applicability,we deploy semantic segmentation of rail surface defects,leveraging connected component processing techniques to distinguish varied defects within the same frame.By computing the actual defect length and area,our deep learning methodology presents results that offer intuitive insights for railway maintenance professionals.
基金supported by the National Key R&D Program of China(No.2021YFF0501102)the National Natural Science Foundation of China(Grants No.52202392,U2368202,52372308,U2468203 and U2468206).
文摘Heavy-haul railways play a vital role in freight transportation,and the health of the rails directly impacts the safety and efficiency of railway operations.The heavy axle loads and long train compositions of heavy-haul trains make the rail surface susceptible to damage such as rail corrugation,spalling and abrasion,threatening operational safety.To address the issue,this paper proposes a multi-source data fusion method for identifying rail surface defects on heavy-haul railways.First,complete ensemble empirical mode decomposition with adaptive noise is used to decompose vibration signals and extract multi-dimensional vibration features.Next,dynamic time warping is applied to align rail profile data and extract key geometric features.Then,the vibration features and profile features are fused using Relief-F to select the most discriminative features.Finally,a support vector machine is utilized for defect identification.Experiment results show that the proposed method achieves high accuracy in identifying rail surface defects,with an accuracy of 96.4%.
