YOLO-O2E:A Variant YOLO Model for Anomalous Rail Fastening Detection

Rail fasteners are a crucial component of the railway transportation safety system.These fasteners,distinguished by their high length-to-width ratio,frequently encounter elevated failure rates,necessitating manual inspection and maintenance.Manual inspection not only consumes time but also poses the risk of potential oversights.With the advancement of deep learning technology in rail fasteners,challenges such as the complex background of rail fasteners and the similarity in their states are addressed.We have proposed an efficient and high-precision rail fastener detection algorithm,named YOLO-O2E(you only look once-O2E).Firstly,we propose the EFOV(Enhanced Field of View)structure,aiming to adjust the effective receptive field size of convolutional kernels to enhance insensitivity to small spatial variations.Additionally,The OD_MP(ODConv and MP_2)and EMA(EfficientMulti-Scale Attention)modules mentioned in the algorithm can acquire a wider spectrum of contextual information,enhancing the model’s ability to recognize and locate objectives.Additionally,we collected and prepared the GKA dataset,sourced from real train tracks.Through testing on the GKA dataset and the publicly available NUE-DET dataset,our method outperforms general-purpose object detection algorithms.On the GKA dataset,our model achieved a mAP 0.5 value of 97.6%and a mAP 0.5:0.95 value of 83.9%,demonstrating excellent inference speed.YOLO-O2E is an algorithm for detecting anomalies in railway fasteners that is applicable in practical industrial settings,addressing the industry gap in rail fastener detection.

Rail Internal Defect Detection Method Based on Enhanced Network Structure and Module Design Using Ultrasonic Images

Improving the detection accuracy of rail internal defects and the generalization ability of detection models are not only the main problems in the field of defect detection but also the key to ensuring the safe operation of high-speed trains.For this reason,a rail internal defect detection method based on an enhanced network structure and module design using ultrasonic images is proposed in this paper.First,a data augmentation method was used to extend the existing image dataset to obtain appropriate image samples.Second,an enhanced network structure was designed to make full use of the high-level and low-level feature information in the image,which improved the accuracy of defect detection.Subsequently,to optimize the detection performance of the proposed model,the Mish activation function was used to design the block module of the feature extraction network.Finally,the pro-posed rail defect detection model was trained.The experimental results showed that the precision rate and F1score of the proposed method were as high as 98%,while the model’s recall rate reached 99%.Specifically,good detec-tion results were achieved for different types of defects,which provides a reference for the engineering application of internal defect detection.Experimental results verified the effectiveness of the proposed method.

Investigation of monitoring system for high-speed railway subgrade frost heave

This paper presents methods for monitoring frost heave, device requirements, testing principals, and data analysis re- quirements, such as manual leveling observation, automatic monitoring （frost heave, frost depth, and moisture）, track dynamic detection, and track status detection. We focused on the requirements of subgrade frost heave monitoring for high speed railways, and the relationship of different monitoring methods during different phases of the railway. The com- prehensive monitoring system of high speed railway subgrade frost heave provided the technical support for dynamic design during construction and safe operation of the rail system.

Feature analysis of acoustic emission sources for rail crack detection by the finite element method

The safety of rail is very important for the development of high speed railway, and it is necessary to investigate the features of inner cracks in rail. In order to obtain the features of Acoustic Emission （AE） sources of inner cracks in rail, AE sources with different types, depths and propagation distances are examined for crack in rail. The finite element method is utilized to model the rail with cracks and the results of experiment demonstrate the effectiveness of this model. Wavelet transform and Rayleigh-Lamb equations are utilized to extract the features of crack AE sources. The results illustrate that the intensity ratio among AE modes can identify the AE source types and the AE sources with different frequencies in rail. There are uniform AE mode features existing in the AE signals from AE sources in rail web, however AE signals from AE sources in rail head and rail base have the complex and unstable AE modes. Different AE source types have the different propagation features in rail. It is helpful to understand the rail cracks and detect the rail cracks based on the AE technique.

Research on the performance of heavy-haul rail breakage detection using track circuit

Rail breakage is one of the major safety risks in railway transportation.Because of the large axle weight,high density and large capacity of the heavy-haul railway,the damage to rail caused by heavy-haul train wheels will be more serious than that caused by ordinary passenger and cargo trains,resulting in a higher frequency of rail breakage.Taking the Daqin Railway Line as the research object,this paper analyses and discusses rail breakages occurring in interstation tracks and in-station tracks by establishing the ZPW-2000A track circuit calculation model considering the land leakage resistance between the rail line tracks;introduces the defining standards and measurement index of the broken rail coefficient to quantitatively analyse the influence of various influencing factors on the rail breakage inspection performance under the most unfavourable working conditions;and compares the model simulation data,the laboratory model data and the field test results to verify its effectiveness,so as to provide a reference and theoretical basis for the subsequent improvement and solution of the heavy-haul railway rail breakage problem.