对深度学习用于城市视觉污染的综述及提出一个实时监控框架与视觉污染指数提案
A Scoping Review of Deep Learning for Urban Visual Pollution and Proposal of a Real-Time Monitoring Framework with a Visual Pollution Index
AI总结：
本文综述了深度学习在城市视觉污染检测中的应用，提出了一种整合视觉污染指数的实时监控框架，旨在提升城市美学和居民福祉。
摘要：
城市视觉污染（UVP）已成为一个关键问题，但关于自动检测和应用的研究仍显零散。本综述映射了现有的基于深度学习的方法，用于检测、分类以及设计一个全面的应用框架，用于视觉污染管理。按照PRISMA-ScR指南，系统搜索和审查了七个学术数据库（Scopus、Web of Science、IEEE Xplore、ACM DL、ScienceDirect、SpringerNatureLink和Wiley），并发现了26篇文章。大多数研究集中在特定污染物类别，并采用YOLO、Faster R-CNN和EfficientDet架构的变体。尽管存在几个数据集，但它们局限于特定区域且缺乏标准化的分类体系。少数研究将检测整合到实时应用系统中，但这些研究往往存在地理偏移。我们提出了一种监控视觉污染的框架，整合了视觉污染指数以评估特定区域的视觉污染严重程度。本综述强调了需要一个统一的UVP管理系统，该系统整合污染物分类、跨城市基准数据集、通用的深度学习模型以及一个支持可持续城市美学并提升城市居民福祉的评估指数。
英文摘要：
Urban Visual Pollution (UVP) has emerged as a critical concern, yet research on automatic detection and application remains fragmented. This scoping review maps the existing deep learning-based approaches for detecting, classifying, and designing a comprehensive application framework for visual pollution management. Following the PRISMA-ScR guidelines, seven academic databases (Scopus, Web of Science, IEEE Xplore, ACM DL, ScienceDirect, SpringerNatureLink, and Wiley) were systematically searched and reviewed, and 26 articles were found. Most research focuses on specific pollutant categories and employs variations of YOLO, Faster R-CNN, and EfficientDet architectures. Although several datasets exist, they are limited to specific areas and lack standardized taxonomies. Few studies integrate detection into real-time application systems, yet they tend to be geographically skewed. We proposed a framework for monitoring visual pollution that integrates a visual pollution index to assess the severity of visual pollution for a certain area. This review highlights the need for a unified UVP management system that incorporates pollutant taxonomy, a cross-city benchmark dataset, a generalized deep learning model, and an assessment index that supports sustainable urban aesthetics and enhances the well-being of urban dwellers.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
