Abstract:
Road damage, particularly cracks and potholes, poses significant risks to
transportation safety and infrastructure sustainability. Traditional
manual inspection methods are time-consuming, costly, and often
unreliable, creating the need for automated solutions. This study proposes
a deep learning-based approach for road damage classification using a
dataset of 1,026 Bangladeshi road images, consisting of 490 crack and 536
pothole samples. Six pretrained convolutional neural network (CNN)
models—MobileNetV2, ResNet50, InceptionV3, DenseNet121, VGG16,
and VGG19—were implemented and evaluated. All models were trained
for 15 epochs with a learning rate of 0.001. Among them, MobileNetV2
achieved the highest performance with an accuracy of 90%, precision of
0.91, recall of 0.90, and F1-score of 0.90, demonstrating its suitability for
resource-efficient real-world deployment. The proposed system offers an
effective solution for timely road maintenance, contributing to improved
road safety, reduced vehicle damage, and sustainable infrastructure
management.
