Railway-CLIP: A multimodal model for abnormal object detection in high-speed railway

Abstract

Automated detection of suspended anomalous objects on high-speed railway catenary systems using computer vision-based technology is a critical task for ensuring railway transportation safety. Despite the critical importance of this task, conventional vision-based foreign object detection methodologies have predominantly concentrated on image data, neglecting the exploration and integration of textual information. The currently popular multimodal model Contrastive Language-Image Pre-training (CLIP) employs contrastive learning to enable simultaneous understanding of both visual and textual modalities. Drawing inspiration from CLIP’s capabilities, this paper introduces a novel CLIP-based multimodal foreign object detection model tailored for railway applications, referred to as Railway-CLIP. This model leverages CLIP’s robust generalization capabilities to enhance performance in the context of catenary foreign object detection. The Railway-CLIP model is primarily composed of an image encoder and a text encoder. Initially, the Segment Anything Model (SAM) is employed to preprocess raw images, identifying candidate bounding boxes that may contain foreign objects. Both the original images and the detected candidate bounding boxes are subsequently fed into the image encoder to extract their respective visual features. In parallel, distinct prompt templates are crafted for both the original images and the candidate bounding boxes to serve as textual inputs. These prompts are then processed by the text encoder to derive textual features. The image and text encoders collaboratively project the multimodal features into a shared semantic space, facilitating the computation of similarity scores between visual and textual representations. The final detection results are determined based on these similarity scores, ensuring a robust and accurate identification of anomalous objects. Extensive experiments on our collected Railway Anomaly Dataset (RAD) demonstrate that the proposed Railway-CLIP outperforms previous state-of-the-art methods, achieving 97.25 % AUROC and 92.66 % F₁-score, thereby validating the effectiveness and superiority of the proposed approach in real-world high-speed railway anomaly detection scenarios.