Vispol: a real-world urban visual pollution dataset for environmental and infrastructure monitoring

Abstract

This study addresses the growing problem of visual pollution in Dhaka, Bangladesh, which includes unauthorized billboards, posters and banners, waste, exposed wires, damaged walls, and deteriorated roads. To support automated monitoring, the research introduces VisPol, a real-world dataset of 1,097 images containing 15,933 annotated instances across six visual pollution categories. Five YOLOv8 models were comparatively evaluated under identical conditions, with YOLOv8x achieving the highest F1-score and mAP@50, YOLOv8l achieving the highest precision and mAP@50–95, and YOLOv8n providing the fastest inference. The study identifies YOLOv8s as offering a practical balance between detection accuracy and computational efficiency. External validation revealed reduced performance across other datasets, highlighting challenges related to annotation differences, object scale, scene composition, and class definitions. Overall, the research provides a standardized dataset and evaluation framework for visual pollution detection, urban environmental monitoring, and smart-city management.