Detection of Foreign Objects Around the Railway Line with YOLOv8

This study proposes a deep learning-based method to detect foreign objects around the railway line. It is important to make this determination with high accuracy for rail transport safety, but traditional methods are disadvantageous in terms of time and cost. In the proposed method, the RailSem19 dataset was used, and a YOLOv8-based model was designed. YOLOv8 is a prominent algorithm in the literature with its fast and accurate object detection capability. In the study, the dataset was diversified using image enhancement techniques. The training, validation, and testing stages used manually labeled data for human and car classes. The training process was carried out through Google Colab and different YOLOv8 sub-architectures were evaluated. The results showed that the YOLOv8m sub-architecture had higher mAP50 values than the other sub-architectures and showed a successful performance in the validation phase. The YOLOv8m model was able to clearly distinguish people and cars around the railway line. The YOLOv8m sub-architecture achieved a mAP50 value of 88.8%. This study presents an automated and efficient method to improve rail transport safety. The high success of the YOLOv8-based model with the RailSem19 dataset can be considered an effective solution to detect potential risks around the railway line.