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

Abstract: 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.

John Paul Q. Tomas, Carlo Miguel P. Legaspi, Karl Anthony S. Dalangin and Gabriel Paul Q. Lim. “Traffic Sign Classification Under Varying Lighting Conditions in the Philippines Using Transfer Learning with ResNet50 and Zero-DCE”. International Journal of Advanced Computer Science and Applications (IJACSA) 17.3 (2026). http://dx.doi.org/10.14569/IJACSA.2026.0170327

@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},
doi = {10.14569/IJACSA.2026.0170327},
url = {http://dx.doi.org/10.14569/IJACSA.2026.0170327},
year = {2026},
publisher = {The Science and Information Organization},
volume = {17},
number = {3},
author = {John Paul Q. Tomas and Carlo Miguel P. Legaspi and Karl Anthony S. Dalangin and Gabriel Paul Q. Lim}
}