An Autonomous Drone-Based Framework for Real-Time Railway Monitoring Using YOLO-Based Defect Detection
Why this work is in the frame
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Bibliographic record
Abstract
Railway infrastructure plays a critical role in transportation systems, and its routine inspection is crucial for ensuring operational stability and safety. Traditional railway inspection methods often rely heavily on fixed sensors and human monitoring, which are expensive to set up and time-consuming, respectively. This paper presents an autonomous drone-based railway monitoring system to detect structural defects and obstructions on railway tracks in real time. The flight stack comprises modern robotic frameworks such as PX4-Autopilot and ROS2. The sensor stack consists of an RGB camera for object detection and a depth camera for altitude estimation. Two parallel object detection pipelines, regular and oriented bounding box (OBB) YOLOv11 models, are fine-tuned to enhance detection accuracy under challenging visual conditions. Simulation results demonstrate the system’s effectiveness in detecting anomalies like sleeper misalignments and railway track obstructions. The system performance is tested with varying model sizes. The YOLOv11n model achieved an F1-score of 0.92 and an average latency of 59 ms per frame, providing a strong balance between accuracy and speed. Controller evaluations across speeds up to 1 m/s showed lateral and yaw RMSEs of 0.30 m and 2.01 deg, respectively, confirming stable and precise navigation. These findings highlight the potential of autonomous aerial systems to supplement or replace traditional railway inspection methods.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it