R2Net: Enhancement of railway severe weather images based on reinforcement learning

The application of visual tasks such as object detection and semantic segmentation in the field of rail transit is becoming increasingly widespread. However, most existing visual systems are based on image design in clear environments, and the problem of degraded images is inevitable in actual rail transit scenes. For example, trains may encounter harsh weather conditions (such as fog and rain) and low light environments such as tunnels and nighttime during their year-round operation, which significantly reduces the clarity and recognizability of images, directly affecting the performance of advanced visual tasks such as object detection and semantic segmentation. This study focuses on the low visibility problem caused by adverse weather conditions (such as fog and rainfall) and weak light conditions in rail transit scenarios, aiming to improve the quality of degraded images through image enhancement technology and enhance the robustness and reliability of visual systems in complex environments. Through targeted image enhancement methods, research aims to restore the detailed information of degraded images, enhance their recognizability, provide higher quality input data for visual tasks in the field of rail transit, and optimize overall system performance.