提出实时监测城市视觉污染的新框架并定义污染指数
A Scoping Review of Deep Learning for Urban Visual Pollution and Proposal of a Real-Time Monitoring Framework with a Visual Pollution Index
- 基于26篇论文综述，整合深度学习检测方法
- 构建包含污染指数的实时监测框架，支持跨城评估
- 适合城市规划与智能环保研究者参考
城市视觉污染（UVP）日益成为重要议题，但自动检测与应用研究仍分散。本综述遵循PRISMA-ScR指南，系统检索7个数据库，筛选出26篇相关论文。多数研究聚焦特定污染类别，采用YOLO、Faster R-CNN和EfficientDet等架构。尽管已有若干数据集，但局限于特定区域且缺乏统一分类体系。少数研究尝试集成到实时系统，但存在地理分布不均问题。本文提出一种视觉污染监测框架，引入视觉污染指数（Visual Pollution Index），用于评估特定区域污染严重程度。研究呼吁建立统一的UVP管理体系，涵盖污染分类标准、跨城市基准数据集、通用深度学习模型及评估指数，以支持可持续城市美学和居民福祉。
原文摘要 · Abstract (English)
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.
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