Automated Railway Track Defect Detection System using YOLO Deep Learning Algorithm

Automated Railway Track Defect Detection System using YOLO Deep Learning Algorithm

Sharin N

1Assistant Professor, Department of Computer Science, IT, AI & ML, Srinivasan College of Arts & Science, Perambalur- 621212, Tamilnadu, India,

Anitha K

2Assistant Professor, Department of Computer Science, IT, AI & ML, Srinivasan College of Arts & Science, Perambalur- 621212, Tamilnadu, India,

ABSTRACT – Railways remain one of the most important modes of transportation, yet many accidents still occur due to unnoticed track deterioration. Cracks, surface deformation, and unexpected obstacles can develop at any time, making traditional manual inspections inadequate because they are slow, costly, and vulnerable to human error. To address these limitations, this work introduces an automated defect detection system based on the YOLO deep learning model. The system is capable of analyzing live video streams and uploaded images, identifying critical defects in real-time, and instantly notifying railway authorities. The full platform is built using Python, OpenCV, Flask, and MySQL, forming a complete monitoring solution with a dashboard, alert system, and historical record analysis. Experimental results show that the model performs reliably under different lighting and environmental conditions, providing high detection accuracy and rapid response. This system significantly enhances track safety and reduces maintenance delays.

Key Words: Railway Track Defect Detection, YOLO Algorithm, Real-Time Monitoring, Computer Vision, Crack and Obstacle Detection

1. INTRODUCTION

   Railway tracks are exposed to continuous stress from heavy trains, environmental factors, and material aging. Over time, these conditions lead to the development of cracks, misalignments, and other structural issues that put both passengers and infrastructure at risk. When such problems are not discovered early, they may escalate into severe failures, often resulting in derailments or service interruptions.

   Conventional track inspections are conducted manually by trained workers who visually evaluate long track sections. Although this method has been used for decades, it has several drawbacks. Inspectors must work for long hours, often in unsafe environments, and the quality of inspection varies depending on human attention, lighting, and weather conditions. Manual inspection also cannot provide continuous, around-the-clock monitoring of the tracks.

   With the rapid advancement of artificial intelligence, computer vision, and deep learning, automated track inspection has become both practical and reliable. YOLO, a fast and accurate object detection algorithm, offers a strong foundation for real-time defect identification. This project uses YOLO to develop a complete automated system capable of detecting multiple types of track faults efficiently and providing immediate alerts.

   The goal of this research is to design a full end-to-end platform that improves safety, reduces manual effort, and ensures timely detection of defects before they lead to critical failures.

2. LITERATURE REVIEW

   Railway inspection techniques have evolved from manual processes to sensor-based systems and, recently, AI-driven solutions. Mohamed Azharudheen, Kumudham, and Kalaivani [6] present a deep learningoptimized data security architecture specifically designed for high-availability big data environments. Their work responds to the limitations of traditional security frameworks, which often struggle to maintain efficiency when deployed in large-scale, distributed systems. By integrating scalable deep learning techniques with lightweight encryption and dynamic protection mechanisms, the authors propose a solution that enhances threat detection while minimizing computational overhead. Their architecture demonstrates how intelligent, adaptive security strategies can preserve both data availability and system performance in demanding big data infrastructures. Mohamed Azharudheen and Vijayalakshmi [8] investigate a novel data protection mechanism aimed at maximizing data availability while preserving strong privacy guarantees in cloud-based environments. Their work identifies the shortcomings of conventional cryptographic models, particularly their computational overhead and vulnerability to key-management issues when applied to large-scale data systems. To address these limitations, the authors introduce a lightweight framework incorporating dynamic shuffling, secret key-sharing, and

   optimized transformation techniques that collectively enhance security without restricting accessibility. Their study provides important insights into how privacy-preserving strategies can be engineered to support both performance efficiency and confidentiality in modern distributed infrastructures.

   1. Traditional Methods

      Manual inspections involve workers walking or riding along the tracks and examining various components such as rails, fasteners, and sleepers. While this method allows close observation, it suffers from inconsistency, fatigue-related errors, and limited inspection frequency. Ultrasonic testing and geometry measurement vehicles provide deeper analysis but require expensive equipment and cannot operate continuously.

   2. Sensor-Based Approaches

      Several modern systems rely on sensors such as accelerometers, infrared thermography, ground-penetrating radar, and eddy current devices. These tools improve accuracy in detecting subsurface defects but often focus on specific fault types and require complex equipment.

   3. Deep Learning Methods

      With advancements in neural networks, researchers have explored CNNs, R-CNN, Faster R-CNN, and GCN-based models for defect detection. CNNs provide strong classification accuracy but struggle with pinpointing defect locations. Two-stage detectors such as R-CNN achieve good precision but are too slow for real-time monitoring.

      YOLO stands out because it processes images in a single step, enabling rapid detection suitable for live video feeds. Studies have shown that YOLO consistently delivers high accuracy while maintaining real-time processing speed, making it an ideal choice for track inspection.

      This research contributes by integrating YOLO into a fully functional system that includes data management, notification features, and a web interfaceelements often missing in previous studies.

3. PROPOSED ARCHITECTURE

   The proposed architecture is designed to operate smoothly from data acquisition to alert generation. It contains the following layers:

   1. Input Layer

      • Receives frames from live cameras or images uploaded by users

      • Uses OpenCV for video capture and frame processing

   2. Preprocessing Layer

      • Resizes and normalizes images

      • Removes noise using filtering techniques

      • Enhances visibility in low-light conditions

      • Performs quality checks for blur and exposure

   3. YOLO Detection Layer

      • Utilizes YOLOv8 for detecting cracks, obstacles, and deformities

      • Produces bounding boxes and confidence values

      • Applies Non-Maximum Suppression to avoid duplicates/p>

   4. Analysis Layer

      • Validates detections using confidence thresholds

      • Determines severity levels (low, medium, high, critical)

      • Matches detections across frames to reduce false positives

   5. Storage Layer

      • MySQL database stores user information, detection logs, and alert data

      • All detection events are recorded for future analysis

   6. Alert Layer

      • Sends immediate notifications through email or SMS

      • Attaches the annotated image showing detected defects

      • Creates acknowledgment records for tracking responses

   7. User Interface Layer

      • Flask-based dashboard

      • Displays real-time video with detections

      • Provides historical reports, filters, and export options

4. Data Flow Diagram

5. METHODOLOGY

   The system follows a clearly structured methodology:

   1. Data Collection

      Visual data is gathered either from surveillance cameras or through user uploads. Continuous monitoring is supported using OpenCVs video streaming tools.

   2. Preprocessing

      The preprocessing stage ensures that all images are uniform and suitable for model inference. Steps include:

      • Resizing to 640×640

      • Normalizing pixel values

      • Filtering noise

      • Enhancing contrast when needed

   3. Model Implementation

      YOLOv8 is selected for its speed and accuracy. The model:

      • Processes inputs in one pass

      • Predicts defect classes and bounding boxes

      • Employs IoU-based NMS to remove redundant predictions

   4. Detection and Verification

      The system evaluates each detection using confidence scores. Real-time mode uses temporal filtering, ensuring that only persistent detections across multiple frames are considered serious.

   5. Data Storage

      Detection results, images, timestamps, and metadata are saved in MySQL tables. This allows detailed record-keeping and later analysis.

   6. Alert Generation

      For high-risk detections:

      • Images are annotated

      • Alerts are sent immediately

      • Messages include defect details and location

   7. User Dashboard

      The Flask interface provides:

      • Real-time video monitoring

      • Detection history pages

      • Export tools

      • User authentication and roles

6. RESULTS

   The system was tested using various datasets and real-time video feeds to evaluate its performance.

   1. Detection Performance

      • Achieved an accuracy of approximately 9295% across major defect categories

      • Efficient in low-light and shadow-heavy environments

      • Successfully identifies small cracks and obstacles

   2. Real-Time Speed

      • Processes between 1825 frames per second on mid-range GPUs

      • Supports uninterrupted 24/7 monitoring

   3. User Interface Performance

      • Dashboard loads detections instantly

      • Annotated images are stored and displayed clearly

      • Operators can review events efficiently

   4. Alert Functionality

      • Email alerts delivered within less than 2 seconds on average

      • Zero missed alerts during testing

   These outcomes confirm that the system can assist railway authorities by providing fast and reliable defect detection.

7. CONCLUSION

This research presents a robust and practical solution for automated railway track defect detection using YOLO. By combining real-time video processing, deep learning, and an interactive dashboard, the system provides far greater coverage and reliability than traditional inspection methods. It ensures faster response times, reduces human workload, and significantly increases track safety.

The integrated alert mechanism and database-backed reporting system make it suitable for real-world deployment. Future advancements may include drone-based monitoring, prediction models for maintenance scheduling, and expansion to additional types of track faults.

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