2024/05/22 by Hai Ni, Rui Wang, Ni, Hai +3
Computer Science · Engineering · #Advanced Algorithms and Applications #Advanced Measurement and Detection Methods #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #Video Surveillance and Tracking Methods
paper · pdf · doi:10.48550/arxiv.2405.13538
openalex publication_date 2024/05/22 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Railway detection is critical for the automation of railway systems. Existing models often prioritize either speed or accuracy, but achieving both remains a challenge. To address the limitations of presetting anchor groups that struggle with varying track proportions from different camera angles, an ultra-fast adaptive track detection network is proposed in this paper. This network comprises a backbone network and two specialized branches (Horizontal Coordinate Locator and Perspective Identifier). The Perspective Identifier selects the suitable anchor group from preset anchor groups, thereby determining the row coordinates of the railway track. Subsequently, the Horizontal Coordinate Locator provides row classification results based on multiple preset anchor groups. Then, utilizing the results from the Perspective Identifier, it generates the column coordinates of the railway track. This network is evaluated on multiple datasets, with the lightweight version achieving an F1 score of 98.68% on the SRail dataset and a detection rate of up to 473 FPS. Compared to the SOTA, the proposed model is competitive in both speed and accuracy. The dataset and code are available at https://github.com/idnihai/UFATD