Multi-Run Statistical Evaluation of Retinex-Net, SNR-Aware, and RetinexFormer for Nighttime Vehicle Re-Identification

Muhammad Rafif (Universitas Kristen Satya Wacana, Salatiga)
Radius Tanone (Universitas Kristen Satya Wacana, Salatiga)

Building of Informatics, Technology and Science
Vol 8 No 2 (2026): September 2026

Nighttime vehicle re-identification (Re-ID) is challenging because low illumination, sensor noise, and motion blur can obscure identity-related visual features in surveillance images. Comparative studies of low-light enhancement (LLE) as a preprocessing step for Re-ID often rely on a single training run, making it difficult to distinguish genuine method effects from training stochasticity. This study evaluates Retinex-Net, SNR-Aware, and RetinexFormer as LLE preprocessing methods for TransReID with a ViT-Base/16 backbone on a stratified nighttime subset of the DN-348 dataset. The subset contains 8,255 images from 150 training identities and 120 shared gallery/query identities, with nighttime degradation simulated using gamma darkening, Poisson-Gaussian noise, and Gaussian blur. Each scenario was trained using three independent random seeds, with performance summarized across runs using mean Average Precision (mAP) and Cumulative Matching Characteristic (CMC) at Rank-1, Rank-5, and Rank-10. Additional statistical validation was performed on a representative run using bootstrap confidence intervals and paired Wilcoxon signed-rank tests with Holm-Bonferroni correction. Image quality was additionally assessed using PSNR, SSIM, and LPIPS, while SIE and JPM were examined through a factorial ablation. RetinexFormer achieved the highest mean mAP of 48.90 ± 0.11%, followed by SNR-Aware at 48.69 ± 0.21% and the baseline at 48.51 ± 0.33%. However, neither RetinexFormer nor SNR-Aware showed a statistically significant improvement over the baseline, whereas Retinex-Net performed significantly worse. RetinexFormer also produced the best image-quality metrics, indicating that stronger visual restoration does not necessarily yield better downstream Re-ID. These findings demonstrate the importance of multi-run and statistically validated evaluation when assessing LLE methods for nighttime vehicle Re-ID. Given that none of the evaluated LLE methods yielded a statistically significant mAP improvement over the no-enhancement baseline, these results indicate that LLE should not be adopted by default as a preprocessing step for nighttime vehicle Re-ID and should only be deployed after scenario-specific multi-run statistical validation.