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Efficient deep learning based rail fastener screw detection method for fastener screw maintenance robot under complex

Yijie Cai^1,2,3,4, Ming He³, Bin Chen⁵

¹School of Mechanical Science and Engineering, Huazhong University of Science and Technology, Wuhan, 430074, China.

Scientific Reports

|November 23, 2024

Summary

This study introduces LFGB-YOLO, a new AI model for detecting rail fastener screws at night under complex lighting conditions. It significantly improves detection accuracy and speed, outperforming existing models.

Keywords:

Complex lighting conditions Fastener screw maintenance robot Rail fastener screw YOLO

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Area of Science:

Computer Vision
Artificial Intelligence
Railway Engineering

Background:

Nighttime rail fastener replacement operations face challenges due to complex lighting conditions (CLCs).
CLCs, caused by multiple light sources like headlamps and flashlights, create dark or low-light areas on screws, hindering fastener recognition algorithms.
Existing algorithms struggle to maintain consistent and optimized lighting for fastener screw detection.

Purpose of the Study:

To develop a novel YOLO-based model for accurate rail fastener screw detection under CLCs.
To improve the efficiency and precision of fastener screw detection algorithms in challenging nighttime environments.

Main Methods:

Proposed the LFGB-YOLO model, comprising a Light-Fast part for efficient feature extraction and a GB-Neck part for lightweight feature fusion.
The Light-Fast part reduces network parameters, FLOPs, and memory access while maintaining high precision.
The GB-Neck part enhances feature fusion networks for accurate information extraction.

Main Results:

LFGB-YOLO demonstrated excellent performance in Recall, mAP@0.5, F1 score, and FPS, surpassing YOLOv5n, YOLOv7-miny, and YOLOv8.
Key metrics showed significant improvements: Recall (+8.9%), mAP@0.5 (+4%), F1-score (+4.8%), and FPS (+8.1%) compared to the baseline model.
The model effectively detects fastener screws even under CLCs.

Conclusions:

LFGB-YOLO offers a robust solution for fastener screw detection in complex nighttime lighting conditions.
The model's superior performance highlights its potential for real-world railway maintenance applications.
This research provides a foundation for future studies on environmental factor-affected object detection.