基于改进DBSCAN的轨道侵限小尺寸障碍物检测方法研究

李梦迪; 李松陶; 郑狄; 邹喜华

doi:10.3788/gzxb20265507.0714004

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基于改进DBSCAN的轨道侵限小尺寸障碍物检测方法研究

激光与激光光学 | 更新时间：2026-06-12

- 基于改进DBSCAN的轨道侵限小尺寸障碍物检测方法研究
- Research on a Small-Sized Obstacle Detection Method for Railway Intrusion Based on Improved DBSCAN
- 光子学报 2026年页码：1-15
- 作者机构：
  
  1.西南交通大学光电融合集成与通信感知教育部重点实验室，成都 611756
  2.西南交通大学信息科学与技术学院，成都 611756
- 作者简介：
  
  李梦迪（2002—），男，硕士，主要研究方向为激光雷达点云处理、点云目标检测。Email：healer@my.swjtu.edu.cn
  郑狄（1982—），男，教授，博士，博士生导师，主要研究方向为激光雷达点云智能处理与分析、新型光电传感器设计与检测技术。Email：dzheng@swjtu.edu.cn
- 基金信息：
  
  四川省科技计划项目(2024ZHCG0190);国家自然科学基金面上项目(62571456);国家重点研发计划项目(2024YFC3015200)
- DOI：10.3788/gzxb20265507.0714004
  中图分类号： TN958.98
- CSTR：32255.14.gzxb20265507.0714004
- 收稿：2026-03-23，
  
  修回：2026-04-27，
  
  录用：2026-04-29，
- 稿件说明：
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李梦迪，李松陶，郑狄，等. 基于改进DBSCAN的轨道侵限小尺寸障碍物检测方法研究［J］.光子学报，2026，55（7）：0714004

LI Mengdi， LI Songtao， ZHENG Di， et al. Research on a Small-Sized Obstacle Detection Method for Railway Intrusion Based on Improved DBSCAN［J］. Acta Photonica Sinica， 2026， 55（7）：0714004
李梦迪，李松陶，郑狄，等. 基于改进DBSCAN的轨道侵限小尺寸障碍物检测方法研究［J］.光子学报，2026，55（7）：0714004 DOI： 10.3788/gzxb20265507.0714004. CSTR： 32255.14.gzxb20265507.0714004.

LI Mengdi， LI Songtao， ZHENG Di， et al. Research on a Small-Sized Obstacle Detection Method for Railway Intrusion Based on Improved DBSCAN［J］. Acta Photonica Sinica， 2026， 55（7）：0714004 DOI： 10.3788/gzxb20265507.0714004. CSTR： 32255.14.gzxb20265507.0714004.

摘要

针对轨道侵限检测中背景环境复杂及激光雷达点云远近密度不均，导致小尺寸障碍物检测精度低、漏检率偏高的问题，提出一种轨道侵限小尺寸障碍物检测方法。首先，利用直通滤波初步处理点云，并联合栅格高程差法与随机抽样一致性算法精确划分铁路限界区域。其次，利用轨道背景与障碍物在点云反射强度上的分布差异，提出基于点云强度直方图的自适应强度阈值搜索算法，通过解析点云强度分布，动态锁定轨道背景消亡边界，为后续分割与聚类提供关键自适应强度参数。进而，采用融合反射强度的轨道平面分割算法实现轨道平面与疑似障碍物点的精准分离。最后，提出一种改进的DBSCAN算法，通过引入自适应邻域半径与核心点强度约束，动态适配点云近密远疏分布特性并抑制铁路限界内的非障碍物点云。实验结果表明：相较于传统DBSCAN算法，所提聚类算法正检率提升64%，所提检测方法在35 m范围内对10 cm障碍物检出率达80%以上，70 m范围内对木块、15 cm障碍物检出率分别达80%、95%以上，80 m范围内对20 cm障碍物检出率达97%以上，且150 m处行人检出率达94%。

Abstract

Ensuring railway operational safety is a paramount priority. However， railway perimeters are frequently threatened by unpredictable foreign object intrusions， such as falling rocks and unauthorized pedestrians， which can precipitate severe accidents. While machine vision is cost-effective， it lacks precise depth information and is highly susceptible to adverse weather. Conversely， LiDAR offers exceptional detection accuracy and the capability to acquire precise three-dimensional spatial information. Yet， existing LiDAR research primarily focuses on large targets， leaving small-sized obstacle detection underexplored. Furthermore， railway LiDAR applications face unique challenges： a "dense-near， sparse-far" point cloud distribution and a highly noisy track background comprising ballast and sleepers. These factors make detecting small-sized obstacles distinctively difficult. To address these limitations， this paper proposes a novel LiDAR-based detection framework for small obstacles intruding into the railway clearance， effectively enhancing identification accuracy amidst the intricate backdrops of railway infrastructure.To effectively isolate obstacle es from the complex background and address the challenge of non-uniform point cloud density， this study establishes a systematic detection framework comprising three primary stages： point cloud preprocessing， track plane segmentation， and adaptive obstacle clustering. First， raw data acquired by the Livox Avia LiDAR is filtered via a pass-through filter to constrain the field of view. Subsequently， a grid-based elevation difference method combined with the Random Sample Consensus （RANSAC） algorithm accurately fits rail lines to define the precise railway boundary zone. Crucially， to address point cloud intensity variations caused by fluctuations in ambient background light， an adaptive intensity threshold search based on a point cloud intensity histogram （AITS-PIH） is introduced. By analyzing the histogram， this algorithm dynamically locks the extinction boundary of the track background intensity， providing a robust adaptive threshold for subsequent stages. Second， an track plane segmentation algorithm fusing reflection intensity separates suspected obstacles from the track background. Overcoming traditional RANSAC's reliance on mere geometric information—which often misclassifies small， near-ground obstacles as track bed points—this method utilizes the adaptive threshold to construct a dual-verification criterion. Points exhibiting significant geometric deviation from the fitted plane or possessing a reflection intensity markedly higher than the background threshold are classified as suspected obstacles. Finally， an improved DBSCAN algorithm clusters these suspected points. It optimizes clustering via an adaptive search radius and a core point identification mechanism. Valid core points must simultaneously exceed the adaptive threshold to filter residual non-obstacle point clouds and meet the minimum neighborhood density requirement. Furthermore， the adaptive neighborhood radius dynamically adjusts linearly based on the point’s spatial distance from the LiDAR， ensuring fine-grained segmentation for dense near-field points while preserving connectivity for sparse far-field points.Based on the typical geometric characteristics of the rail cross-section， as well as the standard track gauge and safety clearance requirements， this study first obtains the precise railway boundary zone. Validation under clear and overcast weather conditions demonstrates the AITS-PIH algorithm's ability to dynamically adjust the intensity threshold， ensuring robust discrimination between the track background and obstacles despite changes in ambient illumination. Real-world railway scene tests reveal impressive results for small obstacle detection： the proposed method boosts the positive recognition rate from 34.05% to 98.13% compared to the traditional baseline. Additionally， the detection method achieves excellent detection rates exceeding 80% for both 10 cm obstacles within 35 m and wood blocks within 70 m. For 15 cm and 20 cm obstacles， the detection rates are maintained at 71% and 100% within the 70 m to 80 m range， respectively. The pedestrian detection rate also reaches 94% at 150 m. Comprehensive testing over 5，000 frames yields a low false alarm rate of 4.82% and an average single-frame processing time of 243 ms， further highlighting the method's applicability. These results verify that the proposed method completely avoids severe missed detections and false alarms caused by traditional geometric-only segmentation， significantly improving small obstacle clustering precision.To address the limitations of existing methods in detecting small-sized obstacles intruding into the railway clearance， this paper proposes a novel LiDAR-based detection framework. The primary contributions include the development of the AITS-PIH for dynamic adaptation to ambient background illumination， an intensity-fused track plane segmentation algorithm， and an improved DBSCAN algorithm. The track plane segmentation algorithm leverages intensity differences induced by laser incidence angles to effectively distinguish track background components from true obstacles， thereby significantly reducing the miss rate at the source. Meanwhile， the improved DBSCAN algorithm incorporates an adaptive neighborhood radius model and core point intensity constraints， enabling the precise clustering of obstacles across varying distances and point densities while effectively suppressing non-obstacle point clouds within the railway boundary zone. Experimental results demonstrate that the improved DBSCAN increases the positive recognition rate by approximately 64 percentage points compared to traditional methods. By maintaining high detection rates for multi-scale obstacles at extended ranges and achieving an average processing time of 243 ms， the proposed system satisfies the stringent requirements for high-precision， real-time obstacle detection in railway safety applications.

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references

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