A Computer Vision System to Localize and Classify Wastes on the Streets

TL;DR

This paper introduces an automated computer vision system that uses deep learning to localize and classify street waste, aiding city cleanliness efforts by providing an objective littering index.

Contribution

It presents a novel deep learning framework and a new dataset for waste detection and classification in urban environments.

Findings

Accurate detection of littering on diverse backgrounds

Effective localization and classification of different waste types

Demonstrated system applicability in real-world scenarios

Abstract

Littering quantification is an important step for improving cleanliness of cities. When human interpretation is too cumbersome or in some cases impossible, an objective index of cleanliness could reduce the littering by awareness actions. In this paper, we present a fully automated computer vision application for littering quantification based on images taken from the streets and sidewalks. We have employed a deep learning based framework to localize and classify different types of wastes. Since there was no waste dataset available, we built our acquisition system mounted on a vehicle. Collected images containing different types of wastes. These images are then annotated for training and benchmarking the developed system. Our results on real case scenarios show accurate detection of littering on variant backgrounds.