This research paper presents a novel approach to pothole detection using Deep
Learning and Image Processing techniques. The proposed system leverages the
VGG16 model for feature extraction and utilizes a custom Siamese network with
triplet loss, referred to as RoadScan. The system aims to address the critical
issue of potholes on roads, which pose significant risks to road users.
Accidents due to potholes on the roads have led to numerous accidents. Although
it is necessary to completely remove potholes, it is a time-consuming process.
Hence, a general road user should be able to detect potholes from a safe
distance in order to avoid damage. Existing methods for pothole detection
heavily rely on object detection algorithms which tend to have a high chance of
failure owing to the similarity in structures and textures of a road and a
pothole. Additionally, these systems utilize millions of parameters thereby
making the model difficult to use in small-scale applications for the general
citizen. By analyzing diverse image processing methods and various
high-performing networks, the proposed model achieves remarkable performance in
accurately detecting potholes. Evaluation metrics such as accuracy, EER,
precision, recall, and AUROC validate the effectiveness of the system.
Additionally, the proposed model demonstrates computational efficiency and
cost-effectiveness by utilizing fewer parameters and data for training. The
research highlights the importance of technology in the transportation sector
and its potential to enhance road safety and convenience. The network proposed
in this model performs with a 96.12 % accuracy, 3.89 % EER, and a 0.988 AUROC
value, which is highly competitive with other state-of-the-art works.

这篇研究论文介绍了一种使用深度学习和图像处理技术进行坑洼检测的新方法，提出的系统利用 VGG16 模型进行特征提取，并采用自定义的具有三重损失的 Siamese 网络（称为 RoadScan），以解决道路上的坑洼问题对道路使用者的重大风险。