Abstract:
"Research on road pothole detection has been ongoing for the past two decades, with signif
icant progress made in the domain of semantic segmentation for detecting potholes. However,
such approaches require a large amount of data, which can be difficult and expensive to collect.
In this context, the author identifies a research gap in the development of few-shot learning algo
rithms for road pothole detection, which could reduce data-gathering effort and computational
costs.
The primary motivation for this research is to address the limitations of manual visual
inspection, which remains the primary method for detecting road potholes. This process is not
only tedious and time-consuming but also dangerous for inspectors, and the results are often
subjective. Developing a few-shot learning algorithm for pothole detection could offer a safer,
faster, and more objective approach to identifying potholes on roads.
To address this research gap, the author proposes a novel approach that leverages deep
stereo matching networks and image classification DCNNs to detect road potholes using few
shot learning. The proposed approach aims to minimize the need for large, well-annotated
datasets while delivering high-accuracy pothole detection. The research objectives include
conducting a literature survey, specifying project requirements, designing and implementing the
pothole detection model, and testing and evaluating the model’s performance using appropriate
metrics.The scope of the research covers the development of the algorithm and its evaluation
using appropriate metrics, with a focus on minimizing the need for large datasets and delivering
high-accuracy pothole detection."
