The American Society of Civil Engineers has graded Americas infrastructure condition as a C, with the road system receiving a dismal D. Roads are vital to regional economic viability, yet their management, maintenance, and repair processes remain inefficient, relying on outdated manual or laser-based inspection methods that are both costly and time-consuming. With the increasing availability of real-time visual data from autonomous vehicles, there is an opportunity to apply computer vision (CV) methods for advanced road monitoring, providing insights to guide infrastructure rehabilitation efforts. This project explores the use of state-of-the-art CV techniques for road distress segmentation. It begins by evaluating synthetic data generated with Generative Adversarial Networks (GANs) to assess its usefulness for model training. The study then applies Convolutional Neural Networks (CNNs) for road distress segmentation and subsequently examines the transformer-based model MaskFormer. Results show that GAN-generated data improves model performance and that MaskFormer outperforms the CNN model in two metrics: mAP50 and IoU.

It's important to monitor road issues such as bumps and potholes to enhance safety and improve road conditions. Smartphones are equipped with various built - in sensors that offer a cost - effective and straightforward way to assess road quality. However, prog ress in this area has been slow due to the lack of high - quality, standardized datasets. This paper discusses a new dataset created by a mobile app that collects sensor data from devices like GPS, accelerometers, gyroscopes, magnetometers, gravity sensors, and orientation sensors. This dataset is one of the few that integrates Geographic Information System (GIS) data with weather information and video footage of road conditions, providing a comprehensive understanding of road issues with geographic context . The dataset allows for a clearer analysis of road conditions by compiling essential data, including vehicle speed, acceleration, rotation rates, and magnetic field intensity, along with the visual and spatial context provided by GIS, weather, and video dat a. Its goal is to provide funding for initiatives that enhance traffic management, infrastructure development, road safety, and urban planning . Additionally, the dataset will be publicly accessible to promote further research and innovation in smart transp ortation systems.

Infrastructure in smart cities is increasingly monitored by networks of closed circuit television (CCTV) cameras. Roads, bridges and tunnels develop cracks, potholes, and fluid leaks that threaten public safety and require timely repair. Manual inspection is costly and hazardous, and existing automatic systems typically address individual defect types or provide unstructured outputs that cannot directly guide maintenance crews. This paper proposes a comprehensive pipeline that leverages street CCTV streams for multi defect detection and segmentation using the YOLO family of object detectors and passes the detections to a vision language model (VLM) for scene aware summarization. The VLM generates a structured action plan in JSON format that includes incident descriptions, recommended tools, dimensions, repair plans, and urgent alerts. We review literature on pothole, crack and leak detection, highlight recent advances in large vision language models such as QwenVL and LLaVA, and describe the design of our early prototype. Experimental evaluation on public datasets and captured CCTV clips demonstrates that the system accurately identifies diverse defects and produces coherent summaries. We conclude by discussing challenges and directions for scaling the system to city wide deployments.

Road potholes pose significant safety hazards and maintenance challenges, particularly on India's diverse and under-maintained road networks. This paper presents iWatchRoadv2, a fully automated end-to-end platform for real-time pothole detection, GPS-based geotagging, and dynamic road health visualization using OpenStreetMap (OSM). We curated a self-annotated dataset of over 7,000 dashcam frames capturing diverse Indian road conditions, weather patterns, and lighting scenarios, which we used to fine-tune the Ultralytics YOLO model for accurate pothole detection. The system synchronizes OCR-extracted video timestamps with external GPS logs to precisely geolocate each detected pothole, enriching detections with comprehensive metadata, including road segment attribution and contractor information managed through an optimized backend database. iWatchRoadv2 introduces intelligent governance features that enable authorities to link road segments with contract metadata through a secure login interface. The system automatically sends alerts to contractors and officials when road health deteriorates, supporting automated accountability and warranty enforcement. The intuitive web interface delivers actionable analytics to stakeholders and the public, facilitating evidence-driven repair planning, budget allocation, and quality assessment. Our cost-effective and scalable solution streamlines frame processing and storage while supporting seamless public engagement for urban and rural deployments. By automating the complete pothole monitoring lifecycle, from detection to repair verification, iWatchRoadv2 enables data-driven smart city management, transparent governance, and sustainable improvements in road infrastructure maintenance. The platform and live demonstration are accessible at https://smlab.niser.ac.in/project/iwatchroad.

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Potholes on the roads are a serious hazard and maintenance burden. This poses a significant threat to road safety and vehicle longevity, especially on the diverse and under-maintained roads of India. In this paper, we present a complete end-to-end system called iWatchRoad for automated pothole detection, Global Positioning System (GPS) tagging, and real time mapping using OpenStreetMap (OSM). We curated a large, self-annotated dataset of over 7,000 frames captured across various road types, lighting conditions, and weather scenarios unique to Indian environments, leveraging dashcam footage. This dataset is used to fine-tune, Ultralytics You Only Look Once (YOLO) model to perform real time pothole detection, while a custom Optical Character Recognition (OCR) module was employed to extract timestamps directly from video frames. The timestamps are synchronized with GPS logs to geotag each detected potholes accurately. The processed data includes the potholes' details and frames as metadata is stored in a database and visualized via a user friendly web interface using OSM. iWatchRoad not only improves detection accuracy under challenging conditions but also provides government compatible outputs for road assessment and maintenance planning through the metadata visible on the website. Our solution is cost effective, hardware efficient, and scalable, offering a practical tool for urban and rural road management in developing regions, making the system automated. iWatchRoad is available at https://smlab.niser.ac.in/project/iwatchroad

Selçuk University, Computer Engineering Department, Konya, Turkey, stasdemir@selcuk .edu.tr, https://orcid.org/0000 - 0002 - 2433 - 246X Abstract: Potholes cause vehicle damage and traffic accidents, creating serious safety and economic problems. Therefore, early and accurate detection of potholes is crucial. Existing detection methods are usually only based on 2D RGB images and cannot accurately analyze the physical characteristics of potholes. In this paper, a publicly available dataset of RGB - D images (PothRGBD) is created and an impr oved YOLOv8 - based model is proposed for both pothole detection and pothole physical features analysis. The Intel RealSense D415 depth camera was used to collect RGB and depth data from the road surfaces, resulting in a PothRGBD dataset of 1000 images. The data was labeled in YOLO format suitable for segmentation. A novel YOLO model is proposed based on the YOLOv8n - seg architecture, which is structurally improved with Dynamic Snake Convolution (DSConv), Simple Attention Module (SimAM) and Gaussian Error Lin ear Unit (GELU). The proposed model segmented potholes with irregular edge structure more accurately, and performed perimeter and depth measurements on depth maps with high accuracy. With the proposed model, the values increased to 93.7%, 90.4% and 93.8% respectively. Thus, an improvement of 1.96% in precision, 6.13% in recall and 2.07% in mAP was achieved. The proposed model performs pothole detection as well as perimet er and depth measurement with high accuracy and is suitable for real - time applications due to its low model complexity. In this way, a lightweight and effective model that can be used in deep learning - based intelligent transportation solutions has been acq uired. Pothole Detection, YOLOv8 Segmentation, Depth Estimation, Intelligent Transportation Systems, RGB - D Imaging, Deep Learning 1. Introduction Potholes are one of the most common and dangerous types of road surface deterioration. It usually oc curs when water seeps into the asphalt or concrete surface and weakens the sub - layers, then the traffic load erodes the weakened area [1, 2] . Over time, small cracks widen into deep potholes.

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For drivers who endure Britain's crumbling roads daily, there's no doubt we're stuck in an escalating'pothole crisis'. These dangerous holes can injure and even kill cyclists and motorists, and are popping up quicker than they can be filled. Now, interactive graphics reveal the shocking extent of the problem - and scientists think climate change is to blame. Climate organisation Round our Way reveals 952,064 potholes were reported in Britain between January and November last year, marking a five-year high. MailOnline's interactive map, based on the new data, reveals the local authorities with the most pothole reports during the period.

Potholes are a constant and often expensive menace on Britain's roads, but perhaps not for much longer. The first pothole robot is due to hit the streets of Hertfordshire within the next few months to test its autonomous repair technology. The robot, called ARRES (Autonomous Road Repair System) PREVENT uses AI to automatically detect, categorise, and fill in cracks. Should the test be successful, the robot could catch and repair cracks before they become too big to fix quickly. This could make road works cheaper, more efficient, and faster compared with human workers.