Railway safety inspections, a critical component of modern transportation systems, face significant challenges from adverse weather conditions, like fog and rain, which degrade image quality and compromise inspection accuracy. To address this limitation, we propose a novel deep learning-based image dehazing algorithm optimized for outdoor railway environments. Our method integrates adaptive high-pass filtering and bilateral grid processing during the feature extraction phase to enhance detail preservation while maintaining computational efficiency. The framework uniquely combines RGB color channels with atmospheric brightness channels to disentangle environmental interference from critical structural information, ensuring balanced restoration across all spectral components. A dual-attention mechanism (channel and spatial attention modules) is incorporated during feature fusion to dynamically prioritize haze-relevant regions and suppress weather-induced artifacts. Comprehensive evaluations demonstrate the algorithm’s superior performance: On the SOTS-Outdoor benchmark, it achieves state-of-the-art PSNR (35.27) and SSIM (0.9869) scores. When tested on a specialized railway inspection dataset containing 12,840 fog-affected track images, the method attains a PSNR of 30.41 and SSIM of 0.9511, with the SSIM being marginally lower (0.0017) than DeHamer while outperforming other comparative methods in perceptual clarity. Quantitative and qualitative analyses confirm that our approach effectively restores critical infrastructure details obscured by atmospheric particles, improving defect detection accuracy by 18.6 percent compared to non-processed images in simulated inspection scenarios. This work establishes a robust solution for weather-resilient railway monitoring systems, demonstrating practical value for automated transportation safety applications.

Low-altitude radar target recognition is important for intelligent airspace monitoring, unmanned aerial vehicle (UAV) supervision, airport bird-strike prevention, and low-altitude remote sensing. Reliable recognition remains difficult because birds, balloons, and UAVs often produce weak radar responses, share similar trajectory-level signatures, and are difficult to annotate at scale. To address these challenges, this paper proposes a physics-informed semantic prompt learning framework for few-shot low-altitude radar target recognition. The framework converts radar point-track and track measurements into structured textual prompts that combine statistical descriptors, radar-domain physical knowledge, and task-specific instructions. A partially fine-tuned Generative Pre-trained Transformer 2 (GPT-2) encoder is then used to extract semantic representations that preserve motion and scattering-related information. An adaptive feature aggregation module further weights informative hidden states across temporal positions and semantic levels, and a relation-based meta-learning network models query-support similarity for few-shot classification. Experiments on a real low-altitude radar dataset with four target categories, namely birds, balloons, small rotary-wing UAVs, and light rotary-wing UAVs, show that the proposed method consistently outperforms conventional machine learning, deep learning, and representative few-shot baselines. Under the 20-shot setting, it achieves mean 90.85% precision, 90.47% recall, and 90.63% F1-score. The results indicate that embedding radar physical semantics into language-model-based representation learning can improve sample efficiency and recognition robustness for low-altitude radar remote sensing.