A Proposal of Acquiring and Analyzing Method for Distributed Litter on the Street using Smartphone Users as Passive Mobility Sensors

Abstract

With increased environmental protection activities, smartphone-enabled cleaning activities to deter street littering are gaining attention. We propose a method to analyze litter-on-road images captured by a smartphone camera mounted on a bicycle for users who do not require conscious care (Fig. 1). First, the user mounts the smartphone on a bicycle and starts the developed application, which creates a still image by capturing videos. The still images were then categorized using machine learning, and the type of trash was annotated in the images. Finally, to predict the distribution of trash, the probability of its influence on the environment, such as convenience stores and bars, was calculated using the machine learning model. This paper discusses our developed system’s efficacy for acquiring and analyzing methods on the road. As a fast effort, we verify the accuracy of tagging PET bottles, cans, food trays, and masks using a learning model generated by Detectron2.