Smart City Community Watch—Camera-Based Community Watch for Traffic and Illegal Dumping

Author:

Pathak Nupur1,Biswal Gangotri1,Goushal Megha1,Mistry Vraj1,Shah Palak1,Li Fenglian2ORCID,Gao Jerry3ORCID

Affiliation:

1. Department of Applied Data Science, San Jose State University, San Jose, CA 95192, USA

2. College of Electronic Information Engineering, Taiyuan University of Technology, Taiyuan 030024, China

3. Department of Computer Engineering, San Jose State University, San Jose, CA 95192, USA

Abstract

The United States is the second-largest waste generator in the world, generating 4.9 pounds (2.2 kg) of Municipal Solid Waste (MSW) per person each day. The excessive amount of waste generated poses serious health and environmental risks, especially because of the prevalence of illegal dumping practices, including improper waste disposal in unauthorized areas. To clean up illegal dumping, the government spends approximately USD 600 per ton, which amounts to USD 178 billion per year. Municipalities face a critical challenge to detect and prevent illegal dumping activities. Current techniques to detect illegal dumping have limited accuracy in detection and do not support an integrated solution of detecting dumping, identifying the vehicle, and a decision algorithm notifying the municipalities in real-time. To tackle this issue, an innovative solution has been developed, utilizing a You Only Look Once (YOLO) detector YOLOv5 for detecting humans, vehicles, license plates, and trash. The solution incorporates DeepSORT for effective identification of illegal dumping by analyzing the distance between a human and the trash’s bounding box. It achieved an accuracy of 97% in dumping detection after training on real-time examples and the COCO dataset covering both daytime and nighttime scenarios. This combination of YOLOv5, DeepSORT, and the decision module demonstrates robust capabilities in detecting dumping. The objective of this web-based application is to minimize the adverse effects on the environment and public health. By leveraging advanced object detection and tracking techniques, along with a user-friendly web application, it aims to promote a cleaner, healthier environment for everyone by reducing improper waste disposal.

Funder

Shanxi Province Science and Technology Cooperation and Exchange Special Project

Publisher

MDPI AG

Reference39 articles.

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3. Karale, A., Kayat, W., Shiva, A., Hopkins, D., and Nenkova, A. (2022, May 10). Cleaning Up Philly’s Streets: A Cloud-Based Machine Learning Tool to Identify Illegal Trash Dumping. Available online: https://fisher.wharton.upenn.edu/wp-content/uploads/2019/06/PhillyTrash.pdf.

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