Dual-channel feature fusion based image enhancement for low-cost train exterior fault detection

Computer vision is a critical sensing modality for real-time train fault monitoring. However, low-light conditions often compromise the detection reliability of trackside systems. In low-cost setups, insufficient illumination degrades signal-to-noise ratios (SNR) and obscures structural features, rendering automated fault detection unreliable. To ensure robust sensing, this paper proposes a dual-cycle mapping framework for low-light image enhancement. The approach establishes bidirectional mapping between low-light and normal-light domains for adaptive brightness correction and color reconstruction. A Dual-Channel Restoration Unit (DCRU), featuring brightness correction and detail recovery branches, is introduced to mitigate uneven illumination and enhance feature discriminability. Furthermore, a Two-Dimensional Discrete Cosine Transform (2D-DCT) with dynamic weighting fuses frequency-domain features to optimize structural and local representations. Experimental results show the proposed method outperforms existing approaches, achieving a Peak Signal-to-Noise Ratio (PSNR) of 28.08, a Structural Similarity Index Measure (SSIM) of 0.93, and a Learned Perceptual Image Patch Similarity (LPIPS) of 0.03. Integration with the You Only Look Once version 8 (YOLOv8) model demonstrates substantial downstream gains: precision and recall increased by 13% and 15%, respectively, while mean Average Precision (mAP) at an Intersection over Union (IoU) of 0.5, and mAP across the IoU range of 0.5-0.95, improved by 10% and 18%. The proposed method provides a feasible solution for train exterior fault detection.