Anomaly detection for rail running bands, the pattern of wheel–rail contact area, is crucial to analyze composite rail irregularities. This paper presents an all-weather vision-based solution for running-band inspection. However, two major algorithmic challenges restrict inspection effectiveness: 1) the identification of high-quality features for diversified and imbalanced data subject to noise and outliers, and 2) inferring implicit anomaly co-occurrence patterns. We regard overall running-band anomaly detection as a multi-label classification problem and develop a novel deep multi-anomaly recognition network via recurrent attention graphs (RAGRN). The proposed RAGRN consists of two fundamental components, each directly addressing the two major challenges of this paper: 1) Class-specific features are extracted for fine-grained discrimination via split-channel and gradient-guided class-specific attention mechanisms; 2) We develop a multi-anomaly classifier, which effectively captures long-distance correlation features via a recurrent attention graph with visual and statistical guidance for graph propagation, containing prior statistical and image-specific information. The experiments and statistical analyses demonstrate that RAGRN outperforms all related state-of-the-art frameworks and has the potential to be applied to practical inspection.

Effective detection of bus passengers enhances the intelligence and automation of public transportation systems, but it is challenged by complex backgrounds and severe scale imbalances. To address these challenges, we introduce DAEAR-DETR, a novel neural network architecture employing Dual-Attention mechanisms and Echo Accumulative Residuals (EAR) for bus passenger detection. This model features a Dual-Attention Encoder comprising the Low-Level Local Attention Module (LLLAM) and the High-Level Global Attention Module (HLGAM). Additionally, it integrates a Bidirectional Cross-scale Feature-Fusion Module (BCFM) and a decoder. The Echo Accumulative Residual (EAR) combats information degradation by reintroducing initial input sequences throughout the Transformer encoder layers. A Gating Mechanism (GM) within the EAR connections selectively filters and enhances relevant features based on the ongoing feature process. Experimental results demonstrate the effectiveness of our approach. On the custom bus passenger dataset, DAEAR-DETR achieves 71.9% AP50, 73.5% ARL, and 61.6% AR$_{\mathrm {50:95}}$, outperforming Faster R-CNN, YOLOv9, and other DETR-based methods. Furthermore, DAEAR-DETR demonstrates strong generalization on the public Caltech Pedestrian dataset, achieving Log Average Miss Rate (LAMR) of 4.16% for the Reasonable subset, 4.97% for the Small subset, and 27.79% for the Heavy Occlusion subset. A real-world bus passenger dataset is also created with 15,656 images and 76,006 labeled passengers.

Fastener pixel-level detection can provide a reliable basis for the assessment of fastener defects and requires only normal samples. Deep learning technology has been widely used for fastener detection due to its powerful ability in feature extraction and self-learning. However, the performance of many existing deep learning-based methods still requires further improvement as they fall short in producing the accurate pixel-level detection, especially for the fasteners in complicated backgrounds. To tackle this challenge, a novel dual-stream detection network (DSD-Net) based on encoder-decoder architecture is proposed for fastener pixel-level detection. In encoding stage, the enhanced and emphasized features of fastener foreground can be obtained by the dual-stream (i.e., raw image stream and mask image stream) encoder embedding designed feature enhancement module and cascade residual pooling module. In decoding stage, the decoder aggregates the features from dual-stream encoder by the feature enhancement module with skip-connection to improve the final fastener pixel-level detection results. Numerous experiments on the constructed dataset demonstrate that DSD-Net achieves more remarkable detection performance (Precision of 96.54%, Recall of 97.62%, Accuracy of 96.46% and IoU of 94.32%) for fasteners against other state-of-the-arts.

Fast detection of fasteners is important to improve the efficiency of railroad maintenance. However, this task remains challenging due to the limited computing resources of inspection system. To solve the challenge, a new lightweight detection network Op-YOLOv4-tiny is proposed in this paper. The proposed network firstly uses ResBlock-N modules to replace the CSPBlock modules in YOLOv4-tiny to reduce the computation complexity. Then, a large scale feature map ($52\times 52$) is added to obtain more features of fasteners to improve the detection accuracy. Extensive experiments are conducted on the captured railway and subway track images and the results show that Op-YOLOv4-tiny has good performance in terms of detection accuracy and speed. In detail, the detection speed and accuracy reach 408 FPS and 96.8%, respectively. In addition, compared with other detection networks and state-of-the-arts, it achieves the better performance. Thus, our proposed Op-YOLOv4-tiny is with some potential industrial application value for fast detection of fasteners.

Vision-based anomaly inspection plays a crucial role in the efficient maintenance of millions of kilometers of railway, with rail segmentation, a key step in such anomaly detection for providing localization prior. However multi-type rails, those involved in crossings and connections, have highly variable patterns, greatly restricting the performance of standard (straight) rail segmentation methods. Semantic segmentation helps to deal with complex railway scenes and variable patterns, however the noise sensitivity, intra-class differences, and inter-class similarities still challenge the segmentation. Superpixel segmentation can aggregate local similar pixels with precise boundaries, which can offer a weak prior for semantic segmentation for boundary information modeling, intra-class aggregation, and inter-class differentiation, however how to integrate superpixel-level guidance to advance rail segmentation is still challenging. This paper proposes a two-stage transformer-Convolutional Neural Network (CNN)-based segmentation framework. The first stage, Attention-Based Superpixel Segmentation Sub-Network via Boundary Calibration (BCASN), generates railway superpixels by the learning of intra-superpixel consistency and boundary calibration to effectively fit rail boundaries and guide the second-stage rail segmentation. The second stage, Superpixel-Guided Multi-Type Rail Segmentation Sub-Network via Contextual Information Aggregation (CIASSN), captures railway semantics via global and cross-scale context construction, aggregates rail features via directional guidance and structured prior, and makes comprehensive segmentation decisions at superpixel and pixel scales with the learning of superpixel-level context and classification. The experiments demonstrate that the proposed solution achieves 98.71% overall accuracy, 98.44% mIoU, and 87.33% boundary recall in multi-type rail segmentation, significantly extends applicable scenarios, and outperforms all related state-of-the-art methods in rail and road segmentation.

Fastener needs to be monitored and inspected periodically to ensure the rail’s safety due to its easily damaged accessory for railway infrastructure. Recently, Industrial Internet of Things (IIoT) and artificial intelligence (AI)-based visual inspection techniques have been exploited to realize the online inspection of fastener’s fault by using a fault detection IoT vehicle that is mounted with multitype sensors and cameras according to the design of our research team. However, instead of traditional artificial inspection, the AI-based automatic fastener inspection approach is still faced with some challenges, for example, collection of enough samples of faulted fastener. In this article, we propose a cascade learning embedded vision inspection method of rail fastener based on the deep convolutional neural network (DCNN). The proposed method has two steps: 1) region position and 2) fault detection. First, a modified single shot multibox detector (SSD) model is adopted to locate the fastener regions from the captured railway images. Then, a key component detection (KCD) method based on the improved faster region convolutional neural network (RCNN) is proposed to realize the detection of faulted fastener. Extensive experiments are conducted to demonstrate the performance of the proposed method. The experiment results show that the proposed method achieves an average precision of 95.38% and an average recall of 98.62% on fastener detection, which is much better than the manual operation.

In this article, we develop an analytical framework to study the impact of location-based relay selection strategy on the reliability of cognitive relay networks. By utilizing the tool of stochastic geometry, we first derive a closed-form expression for the reliability-enhanced region (RER), where relaying transmission can achieve higher transmission reliability than direct transmission. Then, we adopt the normalized reliability gain (NRG) to quantify the reliability benefit obtained by using relaying transmission compared to direct transmission, and we obtain the spatial distribution of NRG in the RER. Subsequently, by taking the spatial random nature of relays’ distribution into account, we investigate the reliability benefit obtained by secondary networks with the optimal location-based relay selection (OLB-RS) strategy. To reduce the feedback overhead during relay selection, we propose a region-aware relay selection (RA-RS) strategy and obtain the achievable reliability benefit. The results indicate that the reliability is highly dependent on the location of relay, and the OLB-RS strategy is to select the relay closest to the midpoint between the corresponding secondary source and destination.

Rail surface defect inspection based on machine vision faces challenges against the complex background with interference and severe data imbalance. To meet these challenges, in this article, we regard defect detection as a key-point estimation problem and present the proposed attention neural network for rail surface defect detection via consistency of Intersection-over-Union(IoU)-guided center-point estimation (CCEANN). The CCEANN contains two crucial components. The two components are the stacked attention Hourglass backbone via cross-stage fusion of multiscale features (CSFA-Hourglass) and the CASIoU-guided center-point estimation head module (CASIoU-CEHM). Furthermore, the CASIoU-guided center-point estimation head module integrating the delicate coordinate compensation mechanism regresses detection boxes flexibly to adapt to defects’ large-scale variation, in which the proposed CASIoU loss, a loss regressing the consistency of intersection-over-union (IoU), central-point distance, area ratio, and scale ratio between the targeted defect and the predicted defect, achieves higher regression accuracy than state-of-the-art IoU-based losses. The experiments demonstrate that the CCEANN outperforms competitive deep learning-based methods in four surface defect datasets.

The accurate measurement of rail wear is critical in track quality inspection. Usually, by matching the measured profile with the standard one based on the unworn rail waist double circle segment (DCS), and comparing their railhead differences, we obtain the rail wear. However, in the complex field environment, this task becomes tricky. For profile registration, one is that the location of rail waist becomes difficult with lots of outliers mixed in the profile, and the other is that the obtained rail waist could be polluted or incomplete. For wear measurement, the outliers scattered on the railhead could cause serious errors. This paper is devoted to solving the above problems. For problem 1, firstly, the rail waist is located after a preprocessing procedure. Then, utilizing the proposed hybrid model called R-H-ICP, we realize the profile registration with a process from coarse to fine. For problem 2, by using the standard profile and the reconstructed one with wear constraints to serve as the template separately, and computing the distance from each point of railhead on measured profile to the template, both the distinct and the inapparent outliers are detected precisely. The efficiency and superiority of proposed methods were verified by vast experiments. For the former, compared with DCS and ICP, the R-H-ICP not only improves the profile utilization ratio obviously, also guarantees the registration accuracy as far as possible. For the latter, the F1-Measure average score of outlier detection reaches 0.998, which outperforms some classical models markedly.

This article aims to improve the performance of deep learning-based defective fastener inspection method. Due to the defective fasteners are insufficient and far less than the defect-free ones in real railway, it is difficult to train a robust fastener inspection model on such imbalanced dataset. In view of this problem, a novel image generation method called four-discriminator cycle-consistent adversarial network (FD-Cycle-GAN) is proposed to generate the defect fastener images using a large number of defect-free ones. Extensive experiments are conducted on the real fastener images and generated images. Experimental results demonstrate that the defect fastener images generated by our proposed method have better quality and richer diversity than those generated by other state-of-the-art methods. In addition, compared with the CNN-only baseline, the performance of the fastener inspection model trained on the expanded dataset containing the defect fastener images generated by FD-Cycle-GAN is improved significantly. The detection accuracy and relative IMP reach 93.25% and 21.59% respectively.

Visual inspection techniques for rail surface defects have become prevalent approaches to obtain information on rail surface damage. However, uneven illumination leads to illegibility of local information, and the change of the wheel-rail area results in the changeful background of the rail surface, both of which pose challenges to the visual inspection. This paper proposes a novel algorithm that detects rail surface defects via partitioned edge features (PEF). PEF eliminates the effect of uneven illumination by effectively extracting edge features and building homogeneous background on the rail surface. In the process of edge feature extraction, the thresholding based on adaptive partition of rail surface (APRS) plays an indispensable role. In APRS, the rail surface is adaptively partitioned into three types of regions according to the wheel-rail contact degree. After that, the dynamic threshold is set adaptively for each region type on the basis of the prior information of defect proportion. Subsequently, based on neighborhood information and fuzzy decision, the spatial information of adjacent pixels and the direction information of fracture edges are utilized to realize the effective recovery of incomplete defect contours. In addition, defect contours are precisely filled via a flexible combination of morphological hole filling operation and defect region extraction based on improved background difference. The accuracy of this PEF algorithm was confirmed by experiments and comparisons with related algorithms. The experiment results show that PEF detects defects with 92.03% recall and 88.49% precision, which achieves higher accuracy than the established detection algorithms for rail surface defects.

For rail wear measurement, the process of profile registration is critical. Nowadays, the widely used method is the rail waist double circle segment method (DCS). However, this method may be invalid in actual applications because of the influence of the following two factors: one is the outliers mixed into profiles and the other is the profile diversity. The former induces the misalignment between the measured profile and the standard one, and the latter may destroy the integrity of rail waist, which makes that the measured profile cannot be matched with the standard one through DCS. To solve the two problems, a hybrid profile registration method is proposed in this paper. First, by locating new matching primitives to realize coarse registration, we check the profile integrity fast. Then, for complete profile, we rematch its original measured profile with the standard one finely based on DCS to detect and remove the outliers correctly. The efficiency and superiority of the proposed method were verified by numerous experiments. The results show that the average score of F1-Measure for outlier detection reaches 0.95, which outperforms some classical models obviously. Meanwhile, the result of the profile integrity check is also basically coincided with the real face of test zones. Furthermore, the system can run at a speed of 21.95 km/h under our experimental setup, which is far higher than that of the rail maintenance train (up to 5 km/h).

Wi-Fi deployed inside a building can be used for positioning indoor users. A commonly used technology is weighted K-nearest neighbor (WKNN) fingerprint which positions a user based on K nearest reference points measured beforehand. The challenge lies in how to configure the value of K to obtain the best positioning accuracy. In this paper, we propose a self-adaptive WKNN (SAWKNN) algorithm with a dynamic K. By adjusting the value of K based on the signal strength, SAWKNN can obtain a better positioning accuracy than traditional WKNN. In particular, a significant percentage of the SAWKNN positioning makes use of a value K = 1. The performance of the proposed algorithm has been evaluated in real-world experiments.

The dramatic development of Internet of Things (IoT) is not only leading to the spectrum crunch, but is also resulting in exorbitant energy consumption. It is thus desirable to liberate IoT from the constraint of the spectrum scarcity and to rein in the growing energy consumption. Wireless energy harvesting relay-assisted underlay cognitive networks (WEH-CRNs), which combine cognitive radio and wireless energy harvesting techniques to alleviate the spectrum and energy constraints by reusing spare spectrum for data transmissions and harvesting ambient energy for power supplies, are conceived as an efficient solution for massive IoT deployments. However, all existing works about WEH-CRNs did not take into account of the spatial location distribution of nodes, which is very important for energy harvesting and information transmission. Therefore, in this paper, we develop a framework for the design and analysis of WEHCRNs with spatial randomly distributed nodes (WEH-RCRNs). We first propose an efficient relay selection strategy in WEHRCRNs to determine when and which relay should be selected to assist transmission. Then, based on the proposed relay selection strategy, we derive the expression for outage probability to measure the outage performance of WEH-RCRNs. Finally, the impacts of related network parameters on the outage probability is also explored on the basis of our analysis results.

With the ever-increasing spectrum requirements for transmitting explosively growing mobile data, spectrum-efficient solutions need to be integrated into future mobile networks. Spectrum sharing enables the primary system to share licensed spectrum with the secondary system. Thus, it is conceived as an appealing solution for improving spectrum usage to eliminate the spectrum supply-demand gap. In this paper, we develop an efficient transmit power control strategy for underlay spectrum sharing networks with spatially Poisson-distributed primary users. A distinguishing feature of the proposed strategy is that it only requires channel state information and location information of a few primary users close to the secondary transmitter, rather than those for all primary users. Furthermore, we evaluate the outage performance of the secondary system and the interference situation of the primary system under this kind of transmit power control strategy. Numerical results demonstrate that the proposed transmit power control strategy can achieve near-optimal outage performance compared to the perfect power control strategy, while reducing the control complexity and the feedback burden significantly at the same time.