# A Unified Light Framework for Real-time Fault Detection of Freight Train Images

arXiv: https://arxiv.org/abs/2102.00381 · HTML: https://mlsanity.com/paper/2102.00381/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 21 of its 35 references that are in the map.
- First posted on arXiv 31 Jan 2021.

## Builds on (most cited first, 21 of 21)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks](https://mlsanity.com/paper/1506.01497/) (2015)
- [Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift](https://mlsanity.com/paper/1502.03167/) (2015)
- [MobileNetV2: Inverted Residuals and Linear Bottlenecks](https://mlsanity.com/paper/1801.04381/) (2018)
- [SSD: Single Shot MultiBox Detector](https://mlsanity.com/paper/1512.02325/) (2015)
- [YOLOv3: An Incremental Improvement](https://mlsanity.com/paper/1804.02767/) (2018)
- [Cascade R-CNN: Delving into High Quality Object Detection](https://mlsanity.com/paper/1712.00726/) (2017)
- [ShuffleNet V2: Practical Guidelines for Efficient CNN Architecture Design](https://mlsanity.com/paper/1807.11164/) (2018)
- [SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and <0.5MB model size](https://mlsanity.com/paper/1602.07360/) (2016)
- [R-FCN: Object Detection via Region-based Fully Convolutional Networks](https://mlsanity.com/paper/1605.06409/) (2016)
- [Caffe: Convolutional Architecture for Fast Feature Embedding](https://mlsanity.com/paper/1408.5093/) (2014)
- [Single-Shot Refinement Neural Network for Object Detection](https://mlsanity.com/paper/1711.06897/) (2017)
- [Gaussian YOLOv3: An Accurate and Fast Object Detector Using Localization Uncertainty for Autonomous Driving](https://mlsanity.com/paper/1904.04620/) (2019)
- [Pelee: A Real-Time Object Detection System on Mobile Devices](https://mlsanity.com/paper/1804.06882/) (2018)
- [RON: Reverse Connection with Objectness Prior Networks for Object Detection](https://mlsanity.com/paper/1707.01691/) (2017)
- [Object Detection from Scratch with Deep Supervision](https://mlsanity.com/paper/1809.09294/) (2018)
- [Tiny-DSOD: Lightweight Object Detection for Resource-Restricted Usages](https://mlsanity.com/paper/1807.11013/) (2018)
- [Multi-scale Location-aware Kernel Representation for Object Detection](https://mlsanity.com/paper/1804.00428/) (2018)
- [FaultNet: Faulty Rail-Valves Detection using Deep Learning and Computer Vision](https://mlsanity.com/paper/1912.04219/) (2019)

## Built on by (most cited first, 7 of 7)

- [LF-YOLO: A Lighter and Faster YOLO for Weld Defect Detection of X-ray Image](https://mlsanity.com/paper/2110.15045/) (2021)
- [Visual Fault Detection of Multi-scale Key Components in Freight Trains](https://mlsanity.com/paper/2211.14522/) (2022)
- [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)
- [A Lightweight NMS-free Framework for Real-time Visual Fault Detection System of Freight Trains](https://mlsanity.com/paper/2205.12458/) (2022)
- [An Efficient MLP-based Point-guided Segmentation Network for Ore Images with Ambiguous Boundary](https://mlsanity.com/paper/2402.17370/) (2024)
- [Prompt-Driven Lightweight Foundation Model for Instance Segmentation-Based Fault Detection in Freight Trains](https://mlsanity.com/paper/2603.12624/) (2026)

## Abstract (arXiv)

Real-time fault detection for freight trains plays a vital role in guaranteeing the security and optimal operation of railway transportation under stringent resource requirements. 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. We firstly design a novel lightweight backbone (RFDNet) to improve the accuracy and reduce computational cost. Then, we propose a multi region proposal network using multi-scale feature maps generated from RFDNet to improve the detection performance. Finally, we present multi level position-sensitive score maps and region of interest pooling to further improve accuracy with few redundant computations. Extensive experimental results on public benchmark datasets suggest that our RFDNet can significantly improve the performance of baseline network with higher accuracy and efficiency. Experiments on six fault datasets show that our method is capable of real-time detection at over 38 frames per second and achieves competitive accuracy and lower computation than the state-of-the-art detectors.