Performance optimization of rail inspection robot system based on deep vision and machine learning

The inspection of railway infrastructure faces significant challenges due to heterogeneous environmental conditions and non-uniform illumination patterns, leading to suboptimal detection performance in conventional robotic systems. This study develops a multi-stage image enhancement pipeline incorporating adaptive target segmentation and stereoscopic correspondence matching. A cross-sensor calibration protocol establishes precise spatial coordinates for defect localization through binocular disparity analysis. The proposed framework integrates an enhanced YOLOv5 architecture with context-aware attention modules, developing a hierarchical feature learning architecture that combines pyramidal representation with bidirectional multi-scale feature fusion layers. Experimental validation demonstrates 91.5% precision in fastener absence detection with optimized computational efficiency, indicating substantial improvements in automated rail defect diagnostics compared to baseline systems.