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结合深度学习和图像标题来进行视觉污染检测,分类和报告

Haya Almalki¹, Nahlah Algethami²

¹Department of Computer Science, Saudi Electronic University, Riyadh, Saudi Arabia.

Scientific reports

|August 26, 2025

概括

这项研究引入了一个使用深度学习和图像标题的人工智能框架, 该系统提高了城市管理和报告准确性, 以实现可持续发展.

科学领域:

环境科学
计算机科学
城市规划

背景情况:

沙特阿拉伯的快速城市发展,包括沙特愿景2030倡议,导致视觉污染等环境挑战.
目前的VP报告方法依赖于通过在线应用程序手动输入数据,这是容易发生错误的.
需要自动化,准确的系统来监测和管理城市环境质量.

研究的目的:

提出一个人工智能驱动的框架来自动检测,分类和报告视觉污染.
将深度学习模型 (YOLOv5,EfficientDet) 与增强VP检测的组合技术相结合.
使用引导语言图像预训 (BLIP) 来自动生成基于图像的报告描述.

主要方法:

开发了一个结合YOLOv5和EfficientDet模型的AI框架,用于视觉污染检测.
采用组合技术来提高个别深度学习模型的性能.
集成BLIP-2 (特别是BLIP2-Flan-T5-XL) 用于自动图像标题以生成报告描述.
使用了
沙特阿拉伯公共道路视觉污染数据集
为了制定和评估框架.

主要成果:

组合方法实现了0.95的平均精度 (mAP),0.95的回忆,0.91的精度和0.93的F1得分.
在视觉污染检测方面,组合方法超过了单个模型.
基于人类评估,BLIP2-Flan-T5-XL模型在生成城市图像的描述性文本方面表现出80%的准确性.

结论:

拟议的AI框架有效地自动检测和报告视觉污染,大大减少与手动输入相关的错误.
整合深度学习和图像标题使公民监控的报告更容易创建,改善城市管理.
这种以人工智能为导向的方法有助于提高沙特城市的生活质量和促进可持续城市发展.

关键词:

有效的整体图片标题视觉污染没有.

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