Abstract
In recent years, the application of pretrained models in specialized domains has become increasingly important. Traditionally, adapting these models involved fine-tuning their parameters and structures through retraining. However, these fine-tuning methods can be inefficient, particularly when addressing data from specific domains or when modifications are needed in the lower layers of large-scale pretrained models. This study aims to investigate the effectiveness of using pretrained models with frozen weights for downstream tasks in the context of railway track detection, particularly focusing on the railway system. To achieve this, we employed a large-scale semantic segmentation model that had been pretrained on extensive datasets. The models utilized were kept with fixed weights, eliminating the need for retraining. We conducted a comparative analysis of various pretrained models sourced from different datasets to identify the most suitable model for the track detection system. The findings from our experiments revealed the performance metrics of the selected pretrained models, highlighting their effectiveness in the specific domain of railway track detection. Overall, this research demonstrates the practical applicability of pretrained models with frozen weights in specialized fields such as railway systems, offering insights into their usefulness and potential for improving detection algorithms in this domain.