Affiliations: [a]
Department of Computer Science and Engineering, Arunachala College of Engineering for Women, Manavilai, Nagercoil, Tamil Nadu, India
| [b]
Department of Electrical and Electronics Engineering, Arunachala College of Engineering for Women, Manavilai, Nagercoil, Tamil Nadu, India
Correspondence:
[*]
K. Brintha, Department of Computer Science and Engineering, Arunachala College of Engineering for Women, Manavilai, Nagercoil, Tamil Nadu, India. E-mail: [email protected].
Note: [1] FOD-YOLO NET: Fasteners Fault and Object Detection in Railway Tracks Using Deep YOLO Network!
Abstract: Automated railway security systems prevent train collisions with trackside obstructions that cause accidents in high-speed railways. Rail safety is being improved and accident rates reduced through continuous research. A rapid advancement in deep learning has promoted new possibilities for research in this field. In this work, a novel deep learning-based FOD-YOLO net is proposed for detecting the fasteners faults and objects in the railway tracks. There are two basic components in the deep learning-based YOLOv8: the backbone and the head. YOLOv8 utilizes an improved version of the CSPDarknet53 network for detecting objects on the railway track. The head of YOLOv8 consists of EfficientNet with various convolutional layers with squeeze and excitation blocks for detecting any defect in the track fasteners. These layers are liable for detecting the objectness scores, bounding boxes and class probabilities structured with fully connected layers for the objects and faults in tracks. Based on the results from the Yolo network, the alert message is sent to the loco pilot to avoid accidents using fuzzy logic. The experimental fallouts of proposed FOD-YOLO net achieve higher accuracy and yields better evaluation results with 98.14% accuracy, 98.84% precision and 95.94% recall. From the experimental results, the FOD-YOLO net improves the overall accuracy range by 5.44%, 4.72%, 0.73%, and 13.18% better than Fast RCNN, YOLOv5s-VF, YOLO-GD, and 2D-SSA + Deep network respectively.
