Abstract: With the spread of Covid-19, more people wear personal protective equipment such as gloves and masks. However, they are littering them all over streets, parking lots and parks. This impacts the environment and damages especially the marine ecosystem. Thus, this waste should not be discarded in the environment. Moreover, it should not be recycled with other plastic materials. Actually, they have to be separated from regular trash collection. Furthermore, littering gloves and masks yields more workload for street cleaners and presents potential harm for them. In this paper, we design a computer vision system for a street sweeper robot that picks up the masks and gloves and disposes them safely in garbage containers. This system relies on Deep Learning techniques for object recognition. In particular, three Deep Learning models will be investigated. They are: You Only Look Once (YOLO) model, Faster Region based Convolutional Neural Network (Faster R-CNN) and DeepLab v3+. The experiment results showed that YOLO is the most suitable approach to design the proposed system. Thus, the performance of the proposed system is 0.94 as F1 measure, 0.79 as IoU, 0.94 as mAP, and 0.41 s as Time to process one image.

Ouiem Bchir, Sultana Almasoud, Lina Alyahya, Renad Aldhalaan, Lina Alsaeed and Nada Aldalbahi, “A Computer Vision System for Street Sweeper Robot” International Journal of Advanced Computer Science and Applications(IJACSA), 13(10), 2022. http://dx.doi.org/10.14569/IJACSA.2022.0131046

@article{Bchir2022,
title = {A Computer Vision System for Street Sweeper Robot},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2022.0131046},
url = {http://dx.doi.org/10.14569/IJACSA.2022.0131046},
year = {2022},
publisher = {The Science and Information Organization},
volume = {13},
number = {10},
author = {Ouiem Bchir and Sultana Almasoud and Lina Alyahya and Renad Aldhalaan and Lina Alsaeed and Nada Aldalbahi}
}