Visual Fault Detection of Multi-scale Key Components in Freight Trains

• URL: http://arxiv.org/abs/2211.14522v1
• Date: Sat, 26 Nov 2022 09:20:49 GMT
• Title: Visual Fault Detection of Multi-scale Key Components in Freight Trains
• Authors: Yang Zhang, Yang Zhou, Huilin Pan, Bo Wu, and Guodong Sun
• Abstract summary: This paper proposes a lightweight anchor-free framework for train fault detectors. We introduce a lightweight backbone and adopt an anchor-free method for localization and regression. Our framework achieves 98.44% accuracy while the model size is only 22.5 MB, outperforming state-of-the-art detectors.
• Score: 9.447245934910063
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Fault detection for key components in the braking system of freight trains is critical for ensuring railway transportation safety. Despite the frequently employed methods based on deep learning, these fault detectors are highly reliant on hardware resources and are complex to implement. In addition, no train fault detectors consider the drop in accuracy induced by scale variation of fault parts. This paper proposes a lightweight anchor-free framework to solve the above problems. Specifically, to reduce the amount of computation and model size, we introduce a lightweight backbone and adopt an anchor-free method for localization and regression. To improve detection accuracy for multi-scale parts, we design a feature pyramid network to generate rectangular layers of different sizes to map parts with similar aspect ratios. Experiments on four fault datasets show that our framework achieves 98.44% accuracy while the model size is only 22.5 MB, outperforming state-of-the-art detectors.

Related papers

• Renormalized Connection for Scale-preferred Object Detection in Satellite Imagery [51.83786195178233]
  We design a Knowledge Discovery Network (KDN) to implement the renormalization group theory in terms of efficient feature extraction. Renormalized connection (RC) on the KDN enables synergistic focusing'' of multi-scale features. RCs extend the multi-level feature's divide-and-conquer'' mechanism of the FPN-based detectors to a wide range of scale-preferred tasks.
  arXiv  Detail & Related papers  (2024-09-09T13:56:22Z)
• WiNet: Wavelet-based Incremental Learning for Efficient Medical Image Registration [68.25711405944239]
  Deep image registration has demonstrated exceptional accuracy and fast inference. Recent advances have adopted either multiple cascades or pyramid architectures to estimate dense deformation fields in a coarse-to-fine manner. We introduce a model-driven WiNet that incrementally estimates scale-wise wavelet coefficients for the displacement/velocity field across various scales.
  arXiv  Detail & Related papers  (2024-07-18T11:51:01Z)
• Global Context Aggregation Network for Lightweight Saliency Detection of Surface Defects [70.48554424894728]
  We develop a Global Context Aggregation Network (GCANet) for lightweight saliency detection of surface defects on the encoder-decoder structure. First, we introduce a novel transformer encoder on the top layer of the lightweight backbone, which captures global context information through a novel Depth-wise Self-Attention (DSA) module. The experimental results on three public defect datasets demonstrate that the proposed network achieves a better trade-off between accuracy and running efficiency compared with other 17 state-of-the-art methods.
  arXiv  Detail & Related papers  (2023-09-22T06:19:11Z)
• DETR Doesn't Need Multi-Scale or Locality Design [69.56292005230185]
  This paper presents an improved DETR detector that maintains a "plain" nature. It uses a single-scale feature map and global cross-attention calculations without specific locality constraints. We show that two simple technologies are surprisingly effective within a plain design to compensate for the lack of multi-scale feature maps and locality constraints.
  arXiv  Detail & Related papers  (2023-08-03T17:59:04Z)
• Efficient Visual Fault Detection for Freight Train Braking System via Heterogeneous Self Distillation in the Wild [8.062167870951706]
  This paper proposes a heterogeneous self-distillation framework to ensure detection accuracy and speed. We employ a novel loss function that makes the network easily concentrate on values near the label to improve learning efficiency. Our framework can achieve over 37 frames per second and maintain the highest accuracy in comparison with traditional distillation approaches.
  arXiv  Detail & Related papers  (2023-07-03T01:27:39Z)
• Lightweight wood panel defect detection method incorporating attention mechanism and feature fusion network [9.775181958901326]
  We propose a lightweight wood panel defect detection method called YOLOv5-LW, which incorporates attention mechanisms and a feature fusion network. The proposed method achieves a 92.8% accuracy rate, reduces the number of parameters by 27.78%, compresses computational volume by 41.25%, improves detection inference speed by 10.16%.
  arXiv  Detail & Related papers  (2023-06-21T08:55:45Z)
• Efficient Decoder-free Object Detection with Transformers [75.00499377197475]
  Vision transformers (ViTs) are changing the landscape of object detection approaches. We propose a decoder-free fully transformer-based (DFFT) object detector. DFFT_SMALL achieves high efficiency in both training and inference stages.
  arXiv  Detail & Related papers  (2022-06-14T13:22:19Z)
• A Lightweight NMS-free Framework for Real-time Visual Fault Detection System of Freight Trains [11.195801283133994]
  Real-time vision-based system of fault detection (RVBS-FD) for freight trains is an essential part of ensuring railway transportation safety. Most existing vision-based methods still have high computational costs based on convolutional neural networks. We propose a lightweight NMS-free framework to achieve real-time detection and high accuracy simultaneously.
  arXiv  Detail & Related papers  (2022-05-25T03:07:48Z)
• The KFIoU Loss for Rotated Object Detection [115.334070064346]
  In this paper, we argue that one effective alternative is to devise an approximate loss who can achieve trend-level alignment with SkewIoU loss. Specifically, we model the objects as Gaussian distribution and adopt Kalman filter to inherently mimic the mechanism of SkewIoU. The resulting new loss called KFIoU is easier to implement and works better compared with exact SkewIoU.
  arXiv  Detail & Related papers  (2022-01-29T10:54:57Z)
• Automatic Detection of Rail Components via A Deep Convolutional Transformer Network [7.557470133155959]
  We propose a deep convolutional transformer network based method to detect multi-class rail components including the rail, clip, and bolt. Our proposed method simplifies the detection pipeline by eliminating the need of prior settings, such as anchor box, aspect ratio, default coordinates, and post-processing. Results of a comprehensive computational study show that our proposed method outperforms a set of existing state-of-art approaches with large margins.
  arXiv  Detail & Related papers  (2021-08-05T07:38:04Z)
• 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)

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.