The real-time monitoring of wheel size is important technology in ensuring the normal and safe operation of railways. Meanwhile, online dynamic measurement equipment based on structured-light sensors is becoming increasingly available. However, such equipment only has an ``artificial'' measurement mode, collects minimal data, has low reliability, and cannot be used in an on-site complex environment. In this regard, we propose a measurement system based on multisensor data fusion. When a wheel to be measured passes through a rail, two groups of wheel cross-profile acquisition sensors are arranged. Then, two profiles of the measured wheel are reconstructed, aligned together, and segmentation parameterized for a complete profile. On the one hand, they can compensate each other to overcome the data missing in wheel profile. On the other hand, it improves measurement accuracy by resisting error noise data induces. With the complete wheel cross-profile, the reliable and accurate wheel size parameters are obtained. Furthermore, the feasibility of this method is verified by analyzing possible influencing factors. Real experiments demonstrate the robustness and dynamic measurement accuracy. In conclusion, this measurement system is simple and highly robust for an on-site complex environment and has a promising application value.

In the railway detection systems, the existing online calibration of operational outdoor structured-light vision sensors usually encounters complex lighting, camera equipped with optical filters and large fields of view (FOVs) etc. These problems seriously limit calibration precision and efficiency. A novel onsite line structured-light vision sensor calibration method independent of target reference feature points and which utilizes a multi-tooth free-moving target is proposed. The intersection points are determined as feature points by using a laser irradiated on the target, and the feature points in the target coordinate system are established on the basis of cross-ratio invariant. Then, the 3D coordinates of feature points in the camera coordinate system are calculated by combining the intrinsic parameters. Meanwhile, the method based on the uncertainty model is used to optimize the location deviation of the feature points after compensation, and the equation of the laser plane is finally obtained. High-accuracy calibration of outdoor line structured-light vision sensor can be achieved when moving freely even without the removal of the optical filter and the use of any auxiliary equipment. The measurement accuracy of our method is equivalent to that of other methods using planar target in ideal imaging conditions. In addition, our method can be applied to online wheel-size measurement system and can meet the calibration and measurement requirements in complex onsite railway environment.

As an important part of the online monitoring in electrified railway, pantograph operational pose inspection plays a vital role in pantograph and catenary system (PCS) security. Due to the impact of the complex conditions on the train roof, it is difficult to replace the existing video surveillance with fully intelligent operational pose inspection. Therefore, this article proposes a novel accurate monocular 3-D pantograph horn edge-based pantograph pose inspection method in the power of superior ability of deep learning. It mainly includes two steps: edge detection and pose optimization. First, we highlight a brand-new high-precision subpixel edge prediction network. The downsampling heatmap and offset prediction map in our edge detector are joined to predict full-size edge points and thus improve the accuracy to the subpixel level. In addition, our newly proposed numerical gradient smoothing loss function overcomes the “jaggy” phenomenon of the edge curves. Second, we propose a nonpoint-to-point corresponding 3-D pose measurement method of the monocular camera. In the iterative optimized process, we innovatively apply the chamfer distance as an optimization function to dynamically adjust the correspondence between the reprojection points and the predicted edge points to optimal convergence. Ablation and comparative experiments demonstrate that our method achieves excellent performance (F-score 93.0% in FEP _2.0 (fixed edge probability) with 82 frames per second (FPS), overcoming the complex conditions of the practical online monitoring sequences. The actual pose measurement results further confirm our efficiency, thereby achieving a robust real-time and high-precision monocular 3-D pantograph operating pose inspection.

The automatic inspection of real-time pantograph-and-catenary (PAC) operational status is important for ensuring the safety of the railway power supply system and realizing its intelligent operation and maintenance. However, state-of-the-art contact-loss monitoring methods cannot achieve high precision, high speed, and robust effects under complex conditions, such as frequent line changes, multi-suspended line interference, unknown complex backgrounds, and brightness variations. Therefore, a new two-stage online detection method for PAC operational status is proposed. First, we propose a three-module-assisted high-precision key point positioning detector for all possible intersection points of PAC. It can realize end-to-end PAC contact corner points detection for double branch contact mode in the process of line changes, instead of the original way of tracking and then detecting. Each auxiliary module adopts a minimalist ${3\times 3} $ convolutional layer without pre-training to enhance the feature expression ability of the corresponding layer in the backbone, thus improving the sub-pixel detection accuracy. Second, we propose a topological accuracy compensation algorithm to compensate for the mismatch error between the corner points in the image and the projection of the actual spatial contact point owing to the self-occlusion of PAC, thus improving the positioning precision of the real 3-D contact point. Physical simulation, accuracy verification, and field experiments prove that our PAC status inspection algorithm can achieve accurate conductor height and stagger value measurements under complicated environmental interference, thus ensuring the safety of train operation.

Structured-light vision sensor, as an important tool for obtaining 3D data, is widely used in fields of online high-precision measurement. However, the captured stripe images can show high-dynamic characteristics of low signal-to-noise ratio and uneven brightness due to the complexity of the onsite environment. These conditions seriously affect measurement reliability and accuracy. In this study, a wheel size measurement framework based on a structured-light vision sensor, which has high precision and reliability and is suitable for highly reflective conditions, is proposed. Initially, the quality evaluation criterion of stripe images is established, and the entire stripe is distinguished into high- and low-quality segments. In addition, the multi-scale Retinex theory is adopted to enhance stripe brightness, which improves the reliability of subsequent stripe center extraction. Experiments verify that this approach can remarkably improve measurement reliability and accuracy and has important practical value.

Online autonomous perception of pantograph catenary system status is of great significance for railway autonomous operation and maintenance (RIOM). Image sensors combined with an image processing algorithm can realize the automatic acquisition of the pantograph catenary condition; however, it is difficult to meet the demand of long-term stable condition acquisition, which restricts the implementation of online contact state feedback and the realization of railway automation. This paper proposes an online intelligent perception of the pantograph and catenary system (PCS) status based on parameter adaptation to realize fast and stable state analysis when the train is in long-term operation outdoors. First, according to the feature of the contact point, we used histogram of gradient (HoG) features and one-dimensional signal combined with a KCF tracker as the baseline method. Then, a result discriminator located by L1 and hash similarity constraints was used to construct a closed-loop parameter adaptive localization framework, which retrieves and updates parameters when tracking failure occurs. After that, a pruned RefineDet method was used to detect pantograph horns and sparks, which, together with the contact points localization method, ensure the long-term stability of feature localization in PCS images. Then, based on the stereo cameras model, the three-dimensional trajectory of the whole pantograph body can be reconstructed by the image features, and we obtained pantograph catenary contact parameters including the pantograph slide posture, contact line offset, arc detection, separation detection, etc. Our method has been tested on more than 16,000 collected image pairs and the results show that the proposed method has a better positioning effect than the state-of-art method, and realizes the online acquisition of pantograph catenary contact state, representing a significant contribution to RIOM.