Detect and Evaluate Visual Pollution on Street Imagery Taken of a Moving Vehicle
Rakhi Bharadwaj, Radhika V. Kulkarni, Aditya Thombre, Yash Pramod Gaikwad, Sushil Suri, Umesh Patekar · International Journal on Recent and Innovation Trends in Computing and Communication · 2023
Visual pollution is a growing problem in urban areas. It is important for environmental management to identify, formalize, measure and evaluate visual pollution. This paper presents a study on the development of an automated system for visual pollution classification using street images taken from a moving vehicle. The proposed system uses convolutional neural networks to classify different types of visual pollutants such as graffiti, faded signage, potholes, litter, construction zones, broken signage, poor street lighting, poor billboards, road sand, sidewalk clutter, and unmaintained facades.In this study, we utilized a large dataset of raw sensor camera inputs gathered from a fleet of multiple vehicles in a specific geographical area. Our aim was to develop convolutional neural networks that simulate human learning to classify visual pollutants from these images. The successful implementation of this system would be a significant contribution to the development of urban planning and the strengthening of communities worldwide. Additionally, it could lead to the creation of a "visual pollution score/index" for urban areas, which could serve as a new metric for urban environmental management. Our findings, which we present in this paper, will be a valuable addition to the academic community and the field of computer vision for environmental management applications.