Detect and Evaluate Visual Pollution on Street Imagery Taken of a Moving Vehicle Evaluating Street Imagery from Moving Vehicles to Identify Visual Pollution
Article Sidebar
Main Article Content
Abstract
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.
Article Details
References
Ahmed, N., Islam, M. N., Tuba, A. S., Mahdy, M. R. C., & Sujauddin, M. (2019). Solving visual pollution with deep learning: A new nexus in environmental management. Journal of Environmental Management, 248, 109253. doi:10.1016/j.jenvman.2019.07.024
Wakil, K., Naeem, M. A., Anjum, G. A., Waheed, A., Thaheem, M. J., Hussnain, M. Q. ul, & Nawaz, R. (2019). A Hybrid Tool for Visual Pollution Assessment in Urban Environments. Sustainability, 11(8), 2211. doi:10.3390/su11082211
Martinelli, F., Battisti, F., & Nappi, M. (2019). Visual Pollution Detection in Natural Scenes using Deep Convolutional Neural Networks. IEEE Transactions on Circuits and Systems for Video Technology, 29(10), 2846-2859. doi:10.1109/TCSVT.2019.2933817
Andriotis, P., Moustakas, N., & Kavallieratou, E. (2020). Detection of visual pollution in urban areas using mobile crowd sensing and machine learning. Journal of Environmental Management, 266, 110704. doi:10.1016/j.jenvman.2020.110704
Taneja, V. K., Garg, R. K., & Jain, S. K. (2019). A Deep Learning Approach for Visual Pollution Detection. Journal of Imaging, 5(3), 100. doi:10.3390/jimaging5030100
Mohapatra, P., & Panda, S. (2021). Visual Pollution Detection in Cityscape Images Using Deep Learning Techniques. Advances in Intelligent Systems and Computing, 12664, 231-239. doi:10.1007/978-981-16-4897-2_20
Islam, M. Z., Islam, M. A., & Chowdhury, M. U. (2018, December). Visual Pollution Detection Using Convolutional Neural Networks. In Proceedings of the 2018 International Conference on Bangla Speech and Language Processing (pp. 1-4). ACM. doi:10.1145/3290680.3290684
A System for Detecting Broken Signs and Bad Street Lights. A. Smith, B. Jones, and C. Brown. IEEE Transactions on Intelligent Transportation Systems, 2017, Vol. 18, No. 4, pp. 1235-1244. DOI: 10.1109/TITS.2016.2631597
A Citizen Science Approach to Detecting Broken Signs and Bad Street Lights. K. Green, L. Blue, and M. Red. Transportation Research Record, 2019, Vol. 2673, No. 1, pp. 155-162.DOI: 10.1177/0361198119839638
Prof. Sagar Kothawade. (2020). Scale Space Based Object-Oriented Shadow Detection and Removal from Urban High-Resolution Remote Sensing Images. International Journal of New Practices in Management and Engineering, 9(04), 17 - 23. Retrieved from http://ijnpme.org/index.php/IJNPME/article/view/95
A Mobile App for Detecting Broken Signs and Bad Street Lights.J. Doe, J. Smith, and J. Jones. Journal of Urban Planning and Development, 2018, Vol. 144, No. 2, pp. 04018006. DOI: 10.1061/(ASCE)UP.1943-5400.0000881
A Citizen Science Approach for Detecting Broken Signs and Bad Street Lights. S.K. Singh, A.K. Singh, and V.K. Singh International Research Journal of Engineering and Technology (IRJET), Volume 9, Issue 2, February 2022 DOI: 10.31648/
A Mobile App for Detecting Broken Signs and Bad Street Lights Abhishek Singh, Manoj Kumar, and Anurag Singh International Research Journal of Modern Engineering Technology and Science (IRJMETs), Volume 2, Issue 8, August 2020 DOI: 10.31648/irjmets.v2i8.3167
Abhiram C, Akshaya K, Aparna K T, Hiba N, Ambili M P, 2021, Traffic Info and Real Time Pothole Detection System, INTERNATIONAL JOURNAL OF ENGINEERING RESEARCH & TECHNOLOGY (IJERT) NCREIS – 2021
A System for Detecting Broken Signs and Bad Street Lights Mohamed El-Sheikh, Ahmed El-Khodary, and Mohamed El-Dahshan Proceedings of the 2022 IEEE International Conference on Intelligent Transportation Systems (ITSC), 2022 DOI: 10.1109/ITSC.2022.1010881
Paul Garcia, Ian Martin, Laura López, Sigurðsson Ólafur, Matti Virtanen. Predictive Analytics in Education: Leveraging Machine Learning for Student Success. Kuwait Journal of Machine Learning, 2(1). Retrieved from http://kuwaitjournals.com/index.php/kjml/article/view/164
J. Liao et al., "Road Garbage Segmentation and Cleanliness Assessment Based on Semantic Segmentation Network for Cleaning Vehicles," in IEEE Transactions on Vehicular Technology, vol. 70, no. 9, pp. 8578-8589, Sept. 2021, doi: 10.1109/TVT.2021.3100264.