Deep Neural Network Based Automatic Litter Detection in Desert Areas Using Unmanned Aerial Vehicle Imagery

Document Type

Conference Proceeding

Source of Publication

2023 International Symposium on Networks, Computers and Communications, ISNCC 2023

Publication Date

1-1-2023

Abstract

The United Arab Emirates (UAE) values its relationship with the desert, considering it a crucial part of its heritage and culture. However, the desert faces environmental challenges due to the improper disposal of garbage by visitors and the dumping of waste, as some perceive the desert as an empty wasteland. The rise in tourism exacerbates the problem, as litter negatively impacts the desert's ecology, wildlife, and natural habitats. Traditional litter collection methods involving human patrols are inadequate for the vast desert terrain. Drones equipped with high-resolution cameras offer a potential solution by conducting aerial surveys quickly and efficiently. However, the manual review of drone footage to detect litter is time-consuming. This paper explores the use of deep neural network architectures, such as Faster R-CNN, SSD, and YOLO, to develop litter detection models. These models focus on distinguishing litter from other man-made objects. The training dataset consists of thousands of samples, and the models are evaluated based on their performance in detecting and locating litter in drone images captured at different altitudes and environmental conditions. The evaluation includes objective and subjective analyses. The research aims to alleviate the practical challenges of litter detection in the desert by automating the process through computer vision-based object detection methods.

ISBN

9798350335590

Publisher

IEEE

Disciplines

Computer Sciences

Keywords

Convolutional Neural Networks, Desert Ecology, Desert Environment, Drone Imagery, Environment Protection, Litter Detection, Object Detection, YOLO-V5

Scopus ID

85179840837

Indexed in Scopus

yes

Open Access

no

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