Foreign object intrusion into railway lines poses a significant threat to train operational safety.Current intelligent identification systems encounter substantial challenges in addressing issues such as data scarcity...Foreign object intrusion into railway lines poses a significant threat to train operational safety.Current intelligent identification systems encounter substantial challenges in addressing issues such as data scarcity and foreign object diversity.To address the aforementioned issues,the paper proposes an intelligent method for detecting foreign objects on railway lines based on a large-scale AI model.The study takes into account the model's feature extraction capabilities and generalization performance during pre-training,expands its structural depth and width,and conducts model fine-tuning in line with relevant transfer learning strategies so as to effectively adapt the model to the task of identifying foreign objects on railway lines.The experimental results demonstrate that the detection algorithm,powered by a large-scale AI model,can significantly reduce reliance on annotated data.Even in the face of limited training data and a diverse array of unknown foreign object categories,the algorithm achieves high detection accuracy and real-time performance.This highlights its robust capability to handle unknown and varied foreign object intrusions in complex environments.展开更多
文摘Foreign object intrusion into railway lines poses a significant threat to train operational safety.Current intelligent identification systems encounter substantial challenges in addressing issues such as data scarcity and foreign object diversity.To address the aforementioned issues,the paper proposes an intelligent method for detecting foreign objects on railway lines based on a large-scale AI model.The study takes into account the model's feature extraction capabilities and generalization performance during pre-training,expands its structural depth and width,and conducts model fine-tuning in line with relevant transfer learning strategies so as to effectively adapt the model to the task of identifying foreign objects on railway lines.The experimental results demonstrate that the detection algorithm,powered by a large-scale AI model,can significantly reduce reliance on annotated data.Even in the face of limited training data and a diverse array of unknown foreign object categories,the algorithm achieves high detection accuracy and real-time performance.This highlights its robust capability to handle unknown and varied foreign object intrusions in complex environments.
