Artificial intelligence models play a crucial role in monitoring and maintaining railroad infrastructure by analyzing image data of foreign objects on power transmission lines. However, the availability of publicly accessible datasets for railroad foreign objects is limited, and the rarity of anomalies in railroad image data, combined with restricted data sharing, poses challenges for training effective foreign object detection models. In this paper, the aim is to present a new dataset of foreign objects on railroad transmission lines, and evaluating the overall performance of mainstream detection models in this context. Taking a unique approach and leveraging large-scale models such as ChatGPT (Chat Generative Pre-trained Transformer) and text-to-image generation models, we synthesize a series of foreign object data. The dataset includes 14,615 images with 40,541 annotated objects, covering four common foreign objects on railroad power transmission lines. Through empirical research on this dataset, we validate the performance of various baseline models in foreign object detection, providing valuable insights for the monitoring and maintenance of railroad facilities.

Rail transportation is used extensively in urban areas to reduce CO₂ emission and increase road capacity. As a result, the energy efficiency of rail transportation is becoming a popular research topic. Energy-efficient train operation involves four types of control: maximal traction, cruising, coasting, and maximal braking. With the rapid development of energy storage devices (ESDs), this paper aims to develop an integrated optimization model to obtain the speed trajectory with the constraint of on-board ESD properties such as capacity, initial state of energy (SOE), and the degradation of the on-board ESD. Mixed integer linear programming is implemented on the discretized distance-based model to find the solution. In addition to the optimal speed trajectory, the on-board ESD discharge/charge operation strategy will be obtained. Results show that with high on-board ESD capacity, a train tends to apply more braking to recover more energy by using ESD; high initial SOE leads to more traction operation but undermines the energy-recovering process; degradation of on-board ESD influences the train operation and net energy consumption by affecting discharge/charge frequency. Additionally, in the case with on-board ESD, more than 11.6% of net energy consumption can be reduced compared with the one without on-board ESD. This paper indicates that the proposed method is an effective and robust way to study how on-board ESD influences optimal train operation as well as to achieve optimal train speed trajectory with on-board ESD.

The main source of urban waste is the daily life activities of residents, and the waste sorting of residents’ waste is important for promoting economic recycling, reducing labor costs, and protecting the environment. However, most residents are unable to make accurate judgments about the categories of household waste, which severely limits the efficiency of waste sorting. We have designed an intelligent waste bin that enables automatic waste sorting and recycling, avoiding the extensive knowledge required for waste sorting. To ensure that the waste-classification model is high accuracy and works in real time, GECM-EfficientNet is proposed based on EfficientNet by streamlining the mobile inverted bottleneck convolution (MBConv) module, introducing the efficient channel attention (ECA) module and coordinate attention (CA) module, and transfer learning. The accuracy of GECM-EfficientNet reaches 94.54% and 94.23% on the self-built household waste dataset and TrashNet dataset, with parameters of only 1.23 M. The time of one recognition on the intelligent waste bin is only 146 ms, which satisfies the real-time classification requirement. Our method improves the computational efficiency of the waste-classification model and simplifies the hardware requirements, which contributes to the residents’ waste classification based on intelligent devices.

3-D perception in train operating environments presents significant challenges, as it must ensure both precise distance estimation and computational efficiency to meet stringent braking requirements. To date, existing 3-D detection architectures, which employ dense voxel or pillar representations, encounter challenges of computational inefficiency and accuracy degradation when processing large-scale railway light detection and ranging (LiDAR) data. To address this challenge, we propose RailVoxelDet, a railway-optimized 3-D detector integrating the multifactor dynamic voxel feature encoder (MDVFE) and efficient backbone. Specifically, MDVFE converts point clouds to 2-D sparse voxels, reducing computational complexity. The backbone employs residual bottlenecks with shared full connected layers and sparse convolutions, enhanced by the SimAM-Point module. Additionally, the feature query and matching module (FQMM) is proposed to establish a bottom-up multilevel feature fusion architecture. Experimental results show RailVoxelDet reaches 71.29% mAP on OSDaR23 and 61.94% mAP on AirR24, with 6.42G FLOPs and a 71.42 ms inference time. It outperforms 12 comparison models, delivering state-of-the-art results.

Foreign object detection (FOD) in railway catenary systems is crucial for ensuring operational safety and preventing catastrophic failures. However, current detection frameworks encounter two significant challenges. First, the infrequency of fault events leads to severe data scarcity, hampering the training and validation of robust detection models. Second, although lightweight networks (e.g., MobileNet, YOLO) achieve compactness by compressing channel factors, they struggle to balance local feature extraction with global dependency modeling. To address these challenges, we propose a solution that includes the following: 1) RailFOD23, a publicly available dataset created using generative AI to mitigate data scarcity; and 2) EPRepSADet, a compact detection framework that utilizes a re-parameterizable bottleneck (Re-bottleneck) and lightweight self-attention (LSA) module for efficient FOD. The Re-bottleneck consolidates multi-branch structures into a single-path representation, whereas LSA facilitates element-wise attention modeling to effectively reduce computational complexity. In addition, the efficient detection head further minimizes model complexity through hierarchical semantic modeling. Extensive experiments demonstrate that EPRepSADet achieves a mean Average Precision (mAP) of 92.5% on the RailFOD23 test set, requiring only 1.7G FLOPs, thus outperforming several state-of-the-art baseline models.