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.

翻译：城市视觉污染已成为一个关键问题，但其自动检测与应用方面的研究仍较为零散。本范围综述梳理了现有的基于深度学习的视觉污染检测、分类方法，并设计了一个用于视觉污染管理的综合应用框架。遵循PRISMA-ScR指南，我们系统检索并审查了七个学术数据库（Scopus、Web of Science、IEEE Xplore、ACM DL、ScienceDirect、SpringerNatureLink和Wiley），共发现26篇相关文献。大多数研究聚焦于特定的污染物类别，并采用YOLO、Faster R-CNN和EfficientDet等架构的变体。尽管存在多个数据集，但它们仅限于特定区域且缺乏标准化的分类体系。少数研究将检测集成到实时应用系统中，但这些研究往往存在地域偏差。我们提出了一个监测视觉污染的框架，该框架集成了一个视觉污染指数，用于评估特定区域视觉污染的严重程度。本综述强调了对统一的城市视觉污染管理系统的需求，该系统应包含污染物分类体系、跨城市基准数据集、通用化的深度学习模型以及支持可持续城市美学并提升城市居民福祉的评估指数。