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

arXiv: https://arxiv.org/abs/2211.14522 · HTML: https://mlsanity.com/paper/2211.14522/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 18 of its 31 references that are in the map.
- First posted on arXiv 26 Nov 2022.

## Builds on (most cited first, 18 of 18)

- [Deformable DETR: Deformable Transformers for End-to-End Object Detection](https://mlsanity.com/paper/2010.04159/) (2020)
- [Fast R-CNN](https://mlsanity.com/paper/1504.08083/) (2015)
- [FCOS: Fully Convolutional One-Stage Object Detection](https://mlsanity.com/paper/1904.01355/) (2019)
- [Objects as Points](https://mlsanity.com/paper/1904.07850/) (2019)
- [YOLOX: Exceeding YOLO Series in 2021](https://mlsanity.com/paper/2107.08430/) (2021)
- [CornerNet: Detecting Objects as Paired Keypoints](https://mlsanity.com/paper/1808.01244/) (2018)
- [Bridging the Gap Between Anchor-based and Anchor-free Detection via Adaptive Training Sample Selection](https://mlsanity.com/paper/1912.02424/) (2019)
- [Sparse R-CNN: End-to-End Object Detection with Learnable Proposals](https://mlsanity.com/paper/2011.12450/) (2020)
- [Caffe: Convolutional Architecture for Fast Feature Embedding](https://mlsanity.com/paper/1408.5093/) (2014)
- [RepPoints: Point Set Representation for Object Detection](https://mlsanity.com/paper/1904.11490/) (2019)
- [AutoAssign: Differentiable Label Assignment for Dense Object Detection](https://mlsanity.com/paper/2007.03496/) (2020)
- [What Makes for End-to-End Object Detection?](https://mlsanity.com/paper/2012.05780/) (2020)
- [CentripetalNet: Pursuing High-quality Keypoint Pairs for Object Detection](https://mlsanity.com/paper/2003.09119/) (2020)
- [FoveaBox: Beyond Anchor-based Object Detector](https://mlsanity.com/paper/1904.03797/) (2019)
- [Towards Balanced Learning for Instance Recognition](https://mlsanity.com/paper/2108.10175/) (2021)
- [A Unified Light Framework for Real-time Fault Detection of Freight Train Images](https://mlsanity.com/paper/2102.00381/) (2021)
- [Matrix Nets: A New Deep Architecture for Object Detection](https://mlsanity.com/paper/1908.04646/) (2019)
- [FasteNet: A Fast Railway Fastener Detector](https://mlsanity.com/paper/2012.07968/) (2020)

## Built on by (most cited first, 5 of 5)

- [Spatial-wise Dynamic Distillation for MLP-like Efficient Visual Fault Detection of Freight Trains](https://mlsanity.com/paper/2312.05832/) (2023)
- [Efficient Visual Fault Detection for Freight Train via Neural Architecture Search with Data Volume Robustness](https://mlsanity.com/paper/2405.17004/) (2024)
- [An Efficient MLP-based Point-guided Segmentation Network for Ore Images with Ambiguous Boundary](https://mlsanity.com/paper/2402.17370/) (2024)
- [HyperDefect-YOLO: Enhance YOLO with HyperGraph Computation for Industrial Defect Detection](https://mlsanity.com/paper/2412.03969/) (2024)
- [Prompt-Driven Lightweight Foundation Model for Instance Segmentation-Based Fault Detection in Freight Trains](https://mlsanity.com/paper/2603.12624/) (2026)

## Abstract (arXiv)

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.