Smart Street Litter Detection and Classification Based on Faster R-CNN and Edge Computing
International Journal of Software Engineering and Knowledge Engineering
Cleanliness of city streets has an important impact on city environment and public health. Conventional street cleaning methods involve street sweepers going to many spots and manually confirming if the street needs to be clean. However, this method takes a substantial amount of manual operations for detection and assessment of street's cleanliness which leads to a high cost for cities. Using pervasive mobile devices and AI technology, it is now possible to develop smart edge-based service system for monitoring and detecting the cleanliness of streets at scale. This paper explores an important aspect of cities - how to automatically analyze street imagery to understand the level of street litter. A vehicle (i.e. trash truck) equipped with smart edge station and cameras is used to collect and process street images in real time. A deep learning model is developed to detect, classify and analyze the diverse types of street litters such as tree branches, leaves, bottles and so on. In addition, two case studies are reported to show its strong potential and effectiveness in smart city systems.
National Natural Science Foundation of China
deep leaning, edge computing, Smart city, street cleanliness
Ping Ping, Guoyan Xu, Effendy Kumala, and Jerry Gao. "Smart Street Litter Detection and Classification Based on Faster R-CNN and Edge Computing" International Journal of Software Engineering and Knowledge Engineering (2020): 537-553. https://doi.org/10.1142/S0218194020400045