Traffic Sign Classification Under Varying Lighting Conditions in the Philippines Using Transfer Learning with ResNet50 and Zero-DCE

This study presents a multi-stage transfer learning approach for improving traffic sign recognition performance under both normal and low-light conditions, addressing the gap between existing datasets and the real-world road environments of the Philippines, where poor lighting, faded signs, and unstructured roads are common. A curated local dataset of 7 commonly encountered traffic sign classes comprising approximately 5,000 manually localized images was constructed and split into training, validation, and test sets (70–10–20 ratio). Five model configurations were developed and compared: a VGG-inspired baseline trained from scratch, a standard ResNet50 transfer learning model, a multiphase ResNet50 model pretrained on the GTSRB dataset, and two corresponding variants enhanced using Zero-DCE low-light preprocessing. The baseline achieved 92.17% accuracy, while the standard ResNet50 models performed similarly with and without Zero-DCE (92.10–92.45%). The multiphase ResNet50 significantly improved accuracy to 96.43% by leveraging domain-aligned pretraining, and the highest performance was achieved by its Zero-DCE-enhanced counterpart at 98.21%, showing more balanced metrics and improved recognition stability. These results indicate that low-light enhancement alone does not guarantee better performance, but becomes highly effective when paired with a feature extractor already specialized in traffic sign features. Overall, the proposed multiphase, Zero-DCE–assisted pipeline provides a strong and scalable solution for traffic sign recognition in low-visibility Philippine conditions, with potential applications in ADAS and autonomous driving systems.

@article{Tomas2026,
  title     = {Traffic Sign Classification Under Varying Lighting Conditions in the Philippines Using Transfer Learning with ResNet50 and Zero-DCE},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {17},
  number    = {3},
  year      = {2026},
  publisher = {The Science and Information Organization},
  author    = {John Paul Q. Tomas and Carlo Miguel P. Legaspi and Karl Anthony S. Dalangin and Gabriel Paul Q. Lim},
  doi       = {10.14569/IJACSA.2026.0170327},
  url       = {https://doi.org/10.14569/IJACSA.2026.0170327}
}