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

arXiv: https://arxiv.org/abs/1710.11374 · HTML: https://mlsanity.com/paper/1710.11374/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 31 Oct 2017.

## Built on by (most cited first, 4 of 4)

- [Material Measurement Units for a Circular Economy: Foundations through a Review](https://mlsanity.com/paper/2103.01997/) (2021)
- [pLitterStreet: Street Level Plastic Litter Detection and Mapping](https://mlsanity.com/paper/2401.14719/) (2024)
- [Improving the performance of object detection by preserving label distribution](https://mlsanity.com/paper/2308.14466/) (2023)
- [A Scoping Review of Deep Learning for Urban Visual Pollution and Proposal of a Real-Time Monitoring Framework with a Visual Pollution Index](https://mlsanity.com/paper/2602.09446/) (2026)

## Abstract (arXiv)

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.