Enhancing deep learning–based railway inspection via PSO-guided brightness–contrast optimization

Reliable operation of electrified railway systems depends critically on the pantograph–catenary system (PCS). Existing inspection practices are largely manual or periodic and remain vulnerable to low illumination, background clutter, and thin structural components, limiting robustness and scalability. Although vision-based deep learning is promising, performance often degrades in low-light and complex scenes, while conventional enhancement (e.g., CLAHE) provides limited and inconsistent improvements. This study proposes an illumination-aware PCS inspection framework that integrates Particle Swarm Optimization (PSO)–guided brightness–contrast optimization with a lightweight YOLO detector. A version benchmark selected YOLOv9t as the backbone, achieving the best overall performance (Precision = 0.947, F1-score = 0.913) compared with YOLOv8n and YOLOv11n. Although YOLOv11n has lower FLOPs, PCS inspection is accuracy-critical because detection errors propagate to event-frequency counting and lateral-deviation assessment; therefore, YOLOv9t was fixed for subsequent experiments. PSO is employed to estimates a single dataset-level global enhancement parameter set, improving robustness while maintaining computational efficiency for early-stage field deployment under limited annotated data and edge hardware constraints. The framework was evaluated on a small yet diverse visible-light dataset spanning day/evening/night conditions and challenging locations (station roofs, bracket regions, and transition sections). Comparative evaluation across three configurations—YOLOv9 baseline, YOLOv9 with CLAHE, and YOLOv9 with PSO-tuned brightness/contrast—achieved detection rates of 90%, 79%, and 99%, respectively, with the largest gains on thin, low-contrast structures (contact and messenger wires). The optimized pipeline further enables reliable estimation of lateral wire deviation in compliance with EN 50367 (≤200 mm), supporting safety-critical inspection in low-light and complex-background conditions.