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.

翻译：本研究论文提出了一种基于深度学习和图像处理技术的坑洞检测新方法。所提出的系统利用VGG16模型进行特征提取，并采用带有三元组损失的自定义Siamese网络（称为RoadScan）。该系统旨在解决道路上坑洞这一关键问题——这些坑洞对道路使用者构成重大风险，因路面坑洞引发的交通事故已导致大量事故。尽管彻底消除坑洞十分必要，但这却是一个耗时的过程。因此，普通道路使用者应能从安全距离检测坑洞以避免损害。现有的坑洞检测方法严重依赖目标检测算法，但由于道路与坑洞在结构和纹理上的相似性，这些算法往往具有较高的失败概率。此外，这些系统使用了数百万个参数，使得模型难以在面向普通公民的小规模应用中部署。通过分析多种图像处理方法和各类高性能网络，所提出的模型在准确检测坑洞方面取得了卓越性能。准确率、等错误率、精确率、召回率和AUROC等评估指标验证了该系统的有效性。此外，所提出的模型通过使用更少的参数和训练数据展现了计算效率和成本效益。本研究凸显了技术在交通领域的重要性及其增强道路安全与便利性的潜力。该模型在测试中实现了96.12%的准确率、3.89%的EER和0.988的AUROC值，与当前最先进的研究成果具有高度竞争力。