arXiv:2109.03385
RoadAtlas: Intelligent Platform for Automated Road Defect Detection and Asset Management
원문 초록
With the rapid development of intelligent detection algorithms based on deep learning, much progress has been made in automatic road defect recognition and road marking parsing. This can effectively address the issue of an expensive and time-consuming process for professional inspectors to review the street manually. Towards this goal, we present RoadAtlas, a novel end-to-end integrated system that can support 1) road defect detection, 2) road marking parsing, 3) a web-based dashboard for presenting and inputting data by users, and 4) a backend containing a well-structured database and developed APIs.
함께 살펴볼 논문
공통 태그를 근거로 연결했습니다. 실제 인용 관계나 논문의 우수성을 뜻하지 않습니다.
Deep Learning-based Multi Project InP Wafer Simulation for Unsupervised Surface Defect Detection
Deep Learning Framework for Infrastructure Maintenance: Crack Detection and High-Resolution Imaging of Infrastructure Surfaces
Semi-Supervised Defect Detection via Conditional Diffusion and CLIP-Guided Noise Filtering
skfolio: Portfolio Optimization in Python
How Good Are Synthetic Requirements ? Evaluating LLM-Generated Datasets for AI4RE
100-Day Analysis of USD/IDR Exchange Rate Dynamics Around the 2025 U.S. Presidential Inauguration
토론
이 논문과 연결된 포럼 글이 아직 없습니다.