Rail Detection: An Efficient Row-based Network and A New Benchmark

• URL: http://arxiv.org/abs/2304.05667v1
• Date: Wed, 12 Apr 2023 07:44:50 GMT
• Title: Rail Detection: An Efficient Row-based Network and A New Benchmark
• Abstract summary: We present a real-world railway dataset, Rail-DB, with 7432 pairs of images and annotations. We present an efficient row-based rail detection method, Rail-Net, containing a lightweight convolutional backbone and an anchor classifier. We evaluate the Rail-Net on Rail-DB with extensive experiments, including cross-scene settings and network backbones ranging from ResNet to Vision Transformers.
• Score: 16.811407800603664
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Rail detection, essential for railroad anomaly detection, aims to identify the railroad region in video frames. Although various studies on rail detection exist, neither an open benchmark nor a high-speed network is available in the community, making algorithm comparison and development difficult. Inspired by the growth of lane detection, we propose a rail database and a row-based rail detection method. In detail, we make several contributions: (i) We present a real-world railway dataset, Rail-DB, with 7432 pairs of images and annotations. The images are collected from different situations in lighting, road structures, and views. The rails are labeled with polylines, and the images are categorized into nine scenes. The Rail-DB is expected to facilitate the improvement of rail detection algorithms. (ii) We present an efficient row-based rail detection method, Rail-Net, containing a lightweight convolutional backbone and an anchor classifier. Specifically, we formulate the process of rail detection as a row-based selecting problem. This strategy reduces the computational cost compared to alternative segmentation methods. (iii) We evaluate the Rail-Net on Rail-DB with extensive experiments, including cross-scene settings and network backbones ranging from ResNet to Vision Transformers. Our method achieves promising performance in terms of both speed and accuracy. Notably, a lightweight version could achieve 92.77% accuracy and 312 frames per second. The Rail-Net outperforms the traditional method by 50.65% and the segmentation one by 5.86%. The database and code are available at: https://github.com/Sampson-Lee/Rail-Detection.

Related papers

• Railway Artificial Intelligence Learning Benchmark (RAIL-BENCH): A Benchmark Suite for Perception in the Railway Domain [1.342309454877223]
  RAIL-BENCH is the first perception benchmark suite for the railway domain.<n>It comprises five challenges - rail track detection, object detection, vegetation segmentation, multi-object tracking, and monocular visual odometry.<n>LineAP is a novel segment-based average precision metric that evaluates the geometric accuracy of polyline predictions.
  arXiv  Detail & Related papers  (2026-04-24T12:37:04Z)
• RailSafeNet: Visual Scene Understanding for Tram Safety [0.3437656066916039]
  RailSafeNet is a real-time framework that fuses semantic segmentation, object detection and a distance assessor to highlight track intrusions.<n>Using only monocular video, the system identifies rails, localises nearby objects and classifies their risk by comparing projected distances with the standard 1435mm rail gauge.
  arXiv  Detail & Related papers  (2025-09-15T16:51:21Z)
• Sketch and Refine: Towards Fast and Accurate Lane Detection [69.63287721343907]
  Lane detection is a challenging task due to the complexity of real-world scenarios. Existing approaches, whether proposal-based or keypoint-based, suffer from depicting lanes effectively and efficiently. We present a "Sketch-and-Refine" paradigm that utilizes the merits of both keypoint-based and proposal-based methods. Experiments show that our SRLane can run at a fast speed (i.e., 278 FPS) while yielding an F1 score of 78.9%.
  arXiv  Detail & Related papers  (2024-01-26T09:28:14Z)
• DALNet: A Rail Detection Network Based on Dynamic Anchor Line [5.413443050594989]
  Rail detection is one of the key factors for intelligent train. We propose a rail detection network called DALNet based on dynamic anchor line.
  arXiv  Detail & Related papers  (2023-08-22T12:12:59Z)
• Bridging Vision and Language Encoders: Parameter-Efficient Tuning for Referring Image Segmentation [72.27914940012423]
  We do an investigation of efficient tuning problems on referring image segmentation. We propose a novel adapter called Bridger to facilitate cross-modal information exchange. We also design a lightweight decoder for image segmentation.
  arXiv  Detail & Related papers  (2023-07-21T12:46:15Z)
• TPE-Net: Track Point Extraction and Association Network for Rail Path Proposal Generation [1.9181913148426697]
  We propose a convolutional encoder-decoder architecture called TPE-Net to extract left-right rail pixels from 3D images. It generates strong rail route hypotheses without reliance on camera parameters, 3D data, and geometrical constraints.
  arXiv  Detail & Related papers  (2023-02-11T22:49:06Z)
• Real-time Rail Recognition Based on 3D Point Clouds [3.0980025155565376]
  A real-time rail recognition method based on 3D point clouds is proposed to solve the challenges, such as disorderly, uneven density and large volume of the point clouds. A multi-scale neural network is proposed to generate the prediction results of each voxel and the rail location. The results show that the method has good performance in detecting straight, curved and other complex topologies.
  arXiv  Detail & Related papers  (2022-01-08T01:42:02Z)
• Intelligent Railway Foreign Object Detection: A Semi-supervised Convolutional Autoencoder Based Method [7.557470133155959]
  We develop a semi-supervised convolutional autoencoder based framework that only requires railway track images without prior knowledge on the foreign objects in the training process. The proposed framework is useful for data analytics via the train Internet-of-Things (IoT) systems.
  arXiv  Detail & Related papers  (2021-08-05T07:32:23Z)
• Learnable Online Graph Representations for 3D Multi-Object Tracking [156.58876381318402]
  We propose a unified and learning based approach to the 3D MOT problem. We employ a Neural Message Passing network for data association that is fully trainable. We show the merit of the proposed approach on the publicly available nuScenes dataset by achieving state-of-the-art performance of 65.6% AMOTA and 58% fewer ID-switches.
  arXiv  Detail & Related papers  (2021-04-23T17:59:28Z)
• A Unified Light Framework for Real-time Fault Detection of Freight Train Images [16.721758280029302]
  Real-time fault detection for freight trains plays a vital role in guaranteeing the security and optimal operation of railway transportation. Despite the promising results for deep learning based approaches, the performance of these fault detectors on freight train images are far from satisfactory in both accuracy and efficiency. This paper proposes a unified light framework to improve detection accuracy while supporting a real-time operation with a low resource requirement.
  arXiv  Detail & Related papers  (2021-01-31T05:10:20Z)
• Automatic Counting and Identification of Train Wagons Based on Computer Vision and Deep Learning [70.84106972725917]
  The proposed solution is cost-effective and can easily replace solutions based on radiofrequency identification (RFID) The system is able to automatically reject some of the train wagons successfully counted, as they have damaged identification codes.
  arXiv  Detail & Related papers  (2020-10-30T14:56:54Z)
• Photon-Driven Neural Path Guiding [102.12596782286607]
  We present a novel neural path guiding approach that can reconstruct high-quality sampling distributions for path guiding from a sparse set of samples. We leverage photons traced from light sources as the input for sampling density reconstruction, which is highly effective for challenging scenes with strong global illumination. Our approach achieves significantly better rendering results of testing scenes than previous state-of-the-art path guiding methods.
  arXiv  Detail & Related papers  (2020-10-05T04:54:01Z)
• Anchor-free Small-scale Multispectral Pedestrian Detection [88.7497134369344]
  We propose a method for effective and efficient multispectral fusion of the two modalities in an adapted single-stage anchor-free base architecture. We aim at learning pedestrian representations based on object center and scale rather than direct bounding box predictions. Results show our method's effectiveness in detecting small-scaled pedestrians.
  arXiv  Detail & Related papers  (2020-08-19T13:13:01Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.