Road potholes threaten driving safety and increase infrastructure maintenance costs, while large-scale and timely pothole detection remains challenging in urban road networks. Vehicle-mounted vibration sensing offers a low-cost and scalable solution, however, continuous transmission of raw acceleration streams causes high communication overhead. Also, vibration patterns induced by potholes are often confused with those caused by manholes, speed bumps, and other local road structures. To address these challenges, this paper proposes an edge-cloud collaborative pothole detection framework based on onboard vibration event screening and federated temporal segmentation. At the vehicle side, a Gaussian Mixture Model (GMM)-based module adaptively models background vibration and screens candidate abnormal events from continuous acceleration streams. The onboard module acts as a lightweight high-recall filter and uploads only compact candidate event segments with their contextual information. At the server side, pothole detection is formulated as a point-wise temporal segmentation task. A 1D Attention U-Net is developed to distinguish potholes from vibration-similar road events by capturing multi-scale temporal features and preserving event boundary information. Furthermore, the model is trained under a federated learning framework to exploit distributed multi-vehicle data while accommodating non-IID vehicle data distributions. Experiments on multi-vehicle vibration sensing data show that the proposed framework reduces unnecessary data transmission from smooth road segments and improves fine-grained pothole detection under both centralized and federated settings.

翻译：道路坑洼威胁行车安全并增加基础设施维护成本，但在城市路网中实现大规模及时的坑洼检测仍具挑战性。车载振动传感提供了低成本可扩展的解决方案，然而连续传输原始加速度数据流会造成高昂的通信开销。此外，坑洼引发的振动模式常与窨井盖、减速带及其他局部道路结构引发的振动模式相混淆。针对这些挑战，本文提出一种基于车载振动事件筛选与联邦时序分割的边云协同坑洼检测框架。在车辆端，基于高斯混合模型（GMM）的模块自适应地建模背景振动，并从连续加速度数据流中筛选候选异常事件。该车载模块作为轻量级高召回率滤波器，仅上传包含上下文信息的紧凑候选事件片段。在服务器端，将坑洼检测建模为逐点时序分割任务。开发了一维注意力U-Net网络，通过捕获多尺度时序特征并保留事件边界信息，区分坑洼与振动相似的道路事件。进一步，在联邦学习框架下训练该模型，以利用分布式多车辆数据并适应非独立同分布（non-IID）的车辆数据分布。在多车辆振动传感数据上的实验表明，所提框架减少了来自平整路段的冗余数据传输，并在集中式和联邦设置下提升了细粒度坑洼检测性能。