Framework for railway defect detection oriented to small and blurry object

doi:10.3785/j.issn.1008-973X.2026.11.014

Journal of ZheJiang University (Engineering Science)

2026, Vol. 60

Issue (11): 2453-2461 DOI: 10.3785/j.issn.1008-973X.2026.11.014

Framework for railway defect detection oriented to small and blurry object

Tengfei HUANG1(

),Jiuyuan HUO1,2,*(

),Fang CAO1,Zeyu WU1,Chuile KONG1

1. School of Electronic and Information Engineering, Lanzhou Jiaotong University, Lanzhou 730070, China
2. National Cryosphere Desert Data Center, Lanzhou 730000, China

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Abstract

To address the problems of missed detection of small objects and false detection of blurred objects in railway defect detection, a dynamic multi-scale and texture-aware framework (DMT) was proposed. Through dynamic scale-aware pooling (DSP), texture contrast enhancement (TCE), and blurred boundary awareness (BBA), the model’s feature extraction and boundary representation capabilities were enhanced in complex scenarios. Based on the DMT framework, three lightweight modules, DSP, TCE, and BBA, were designed and integrated into the detection network. The proposed framework was incorporated into YOLOv8 to construct a railway defect detection model termed DMT-YOLOv8. Experimental results on real-world railway defect datasets showed that DMT-YOLOv8 outperformed the baseline model in both small-object and blurred-object detection and exhibited good generality and generalization capability. Specifically, the mAP@0.5 and mAP@0.5:0.95 reached 89.1% and 50.2%, respectively, and the overall performance surpassed that of several mainstream detection methods.

Key words： railway defect detection small object detection blurry object multi-scale feature fusion texture contrast enhancement blurred-boundary awareness

Received: 26 September 2025 Published: 16 October 2026

CLC:

TP 391

Fund: 国家自然科学基金资助项目（62262038）；甘肃省科技计划-中小企业创新基金资助项目（25CXGA014）；甘肃省科技创新引导计划-科技专员专项（25CXGA030）.

Corresponding Authors: Jiuyuan HUO E-mail: 849138454@qq.com;huojy@mail.lzjtu.cn

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Cite this article:

Tengfei HUANG,Jiuyuan HUO,Fang CAO,Zeyu WU,Chuile KONG. Framework for railway defect detection oriented to small and blurry object. Journal of ZheJiang University (Engineering Science), 2026, 60(11): 2453-2461.

URL:

https://www.zjujournals.com/eng/10.3785/j.issn.1008-973X.2026.11.014 OR https://www.zjujournals.com/eng/Y2026/V60/I11/2453

面向小目标与模糊目标的轨道缺陷检测框架

为了解决轨道缺陷检测中小目标易漏检与模糊目标易误检的问题，提出动态多尺度与纹理感知框架（DMT）. 从动态尺度感知池化（DSP）、纹理对比增强（TCE）和模糊边缘感知（BBA）出发，系统提升模型在复杂场景下的特征提取与边缘表征能力. 基于DMT框架，设计DSP、TCE与BBA 3种轻量级子模块融入检测网络. 将所提框架嵌入YOLOv8，构建名为DMT-YOLOv8的轨道缺陷检测模型. 在真实轨道缺陷数据集上的实验结果表明，DMT-YOLOv8在小目标检测与模糊目标检测方面均优于基线模型，具备良好的通用性与泛化性. DMT-YOLOv8的mAP@0.5与mAP@0.5:0.95分别达到89.1%和50.2%，整体性能超越多种主流检测方法.

关键词： 轨道缺陷检测, 小目标检测, 模糊目标, 多尺度特征融合, 纹理对比增强, 边缘模糊感知

Fig.1 Network structure of dynamic scale-aware pooling module

Fig.2 Network structure of efficient channel attention mechanism

Fig.3 Network structure of texture contrast enhancement module

Fig.4 Network structure of blurred boundary awareness module

Fig.5 Architecture of DMT-YOLOv8 model for railway defect detection

Tab.1 Effect of different groups in dynamic scale-aware pooling on detection performance of proposed model

Tab.2 Effect of different convolution kernel sizes in efficient channel attention mechanism on detection performance of proposed model

Tab.3 Effect of different module combinations on model detection performance

Tab.4 Performance comparison of dynamic multi-scale and texture-aware framework on different baseline algorithms

Tab.5 Comparison of detection performance among different algorithms on railway defect detection %

Fig.6 Comparison of PR curves of different detection algorithms

Fig.7 Visual detection results of different algorithms on railway defect dataset

Tab.6 Performance comparison results of two detection algorithms on Rail-1118 dataset %

Fig.8 Visual detection results of two algorithms on Rail-1118 dataset

Fig.9 Examples of detection failures of proposed model

Tab.7 Statistics of detection error types for proposed model


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