Pothole detection is crucial for road safety and maintenance, traditionally
relying on 2D image segmentation. However, existing 3D Semantic Pothole
Segmentation research often overlooks point cloud sparsity, leading to
suboptimal local feature capture and segmentation accuracy. Our research
presents an innovative point cloud-based pothole segmentation architecture. Our
model efficiently identifies hidden features and uses a feedback mechanism to
enhance local characteristics, improving feature presentation. We introduce a
local relationship learning module to understand local shape relationships,
enhancing structural insights. Additionally, we propose a lightweight adaptive
structure for refining local point features using the K nearest neighbor
algorithm, addressing point cloud density differences and domain selection.
Shared MLP Pooling is integrated to learn deep aggregation features,
facilitating semantic data exploration and segmentation guidance. Extensive
experiments on three public datasets confirm PotholeGuard's superior
performance over state-of-the-art methods. Our approach offers a promising
solution for robust and accurate 3D pothole segmentation, with applications in
road maintenance and safety.

我们的研究提出了一种创新的基于点云的坑洞分割架构（PotholeGuard），通过有效地识别隐藏的特征并利用反馈机制增强局部特征，提高特征表达。我们引入了本地关系学习模块以了解局部形状关系，增强结构洞察力。此外，我们提出了一个轻量级的自适应结构，使用 K 最近邻算法来细化局部点特征，解决点云密度差异和域选择问题。集成了共享 MLP 汇聚特征的方法，促进语义数据的探索和分割引导。在三个公共数据集上进行了大量实验，验证了 PotholeGuard 相比于现有先进方法的卓越性能。我们的方法为强大而准确的 3D 坑洞分割提供了有希望的解决方案，具有道路维护和安全应用的潜力。

PotholeGuard: 基于点云语义分割的路面坑洞检测方法

PotholeGuard: A Pothole Detection Approach by Point Cloud Semantic  Segmentation

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。

RoadScan: 一种新颖且稳健的自动驾驶道路坑洞检测的迁移学习框架

RoadScan: A Novel and Robust Transfer Learning Framework for Autonomous  Pothole Detection in Roads

Pothole detection is one of the most important tasks for road maintenance.
Computer vision approaches are generally based on either 2D road image analysis
or 3D road surface modeling. However, these two categories are always used
independently. Furthermore, the pothole detection accuracy is still far from
satisfactory. Therefore, in this paper, we present a robust pothole detection
algorithm that is both accurate and computationally efficient. A dense
disparity map is first transformed to better distinguish between damaged and
undamaged road areas. To achieve greater disparity transformation efficiency,
golden section search and dynamic programming are utilized to estimate the
transformation parameters. Otsu's thresholding method is then used to extract
potential undamaged road areas from the transformed disparity map. The
disparities in the extracted areas are modeled by a quadratic surface using
least squares fitting. To improve disparity map modeling robustness, the
surface normal is also integrated into the surface modeling process.
Furthermore, random sample consensus is utilized to reduce the effects caused
by outliers. By comparing the difference between the actual and modeled
disparity maps, the potholes can be detected accurately. Finally, the point
clouds of the detected potholes are extracted from the reconstructed 3D road
surface. The experimental results show that the successful detection accuracy
of the proposed system is around 98.7% and the overall pixel-level accuracy is
approximately 99.6%.

本篇论文介绍了一种精确可靠且计算效率高的路面坑洞检测算法，该算法基于非常密集的视差图，利用双黄金分割搜索和动态规划来估计转换参数，然后通过二次曲面拟合表征提取潜在未受损路面区域，通过随机抽样共识算法减少异常点带来的干扰，最后利用重建的三维路面模型提取检测的路面坑洞点云。实验结果表明，该方法的成功检测率约为 98.7％，整体精度约为 99.6％。