Enhancing Pavement Crack Classification with Bidirectional Cascaded Neural Networks
Journal:
arXiv
Published Date:
Mar 27, 2025
Abstract
Pavement distress, such as cracks and potholes, is a significant issue
affecting road safety and maintenance. In this study, we present the
implementation and evaluation of Bidirectional Cascaded Neural Networks (BCNNs)
for the classification of pavement crack images following image augmentation.
We classified pavement cracks into three main categories: linear cracks,
potholes, and fatigue cracks on an enhanced dataset utilizing U-Net 50 for
image augmentation. The augmented dataset comprised 599 images. Our proposed
BCNN model was designed to leverage both forward and backward information
flows, with detection accuracy enhanced by its cascaded structure wherein each
layer progressively refines the output of the preceding one. Our model achieved
an overall accuracy of 87%, with precision, recall, and F1-score measures
indicating high effectiveness across the categories. For fatigue cracks, the
model recorded a precision of 0.87, recall of 0.83, and F1-score of 0.85 on 205
images. Linear cracks were detected with a precision of 0.81, recall of 0.89,
and F1-score of 0.85 on 205 images, and potholes with a precision of 0.96,
recall of 0.90, and F1-score of 0.93 on 189 images. The macro and weighted
average of precision, recall, and F1-score were identical at 0.88, confirming
the BCNN's excellent performance in classifying complex pavement crack
patterns. This research demonstrates the potential of BCNNs to significantly
enhance the accuracy and reliability of pavement distress classification,
resulting in more effective and efficient pavement maintenance and management
systems.
