TY  - JOUR
KW  - convolutional neural network; data augmentation; deep learning; garbage image symmetry; unmanned aerial vehicle
Y1  - 2022///
IS  - 5
SN  - 2073-8994
UR  - http://doi.org/10.3390/sym14050960
ID  - uninimx3715
TI  - A Deep Learning-Based Intelligent Garbage Detection System Using an Unmanned Aerial Vehicle
N2  - A population explosion has resulted in garbage generation on a large scale. The process of proper and automatic garbage collection is a challenging and tedious task for developing countries. This paper proposes a deep learning-based intelligent garbage detection system using an Unmanned Aerial Vehicle (UAV). The main aim of this paper is to provide a low-cost, accurate and easy-to-use solution for handling the garbage effectively. It also helps municipal corporations to detect the garbage areas in remote locations automatically. This automation was derived using two Convolutional Neural Network (CNN) models and images of solid waste were captured by the drone. Both models were trained on the collected image dataset at different learning rates, optimizers and epochs. This research uses symmetry during the sampling of garbage images. Homogeneity regarding resizing of images is generated due to the application of symmetry to extract their characteristics. The performance of two CNN models was evaluated with the state-of-the-art models using different performance evaluation metrics such as precision, recall, F1-score, and accuracy. The CNN1 model achieved better performance for automatic solid waste detection with 94% accuracy
A1  - Verma, Vishal
A1  - Gupta, Deepali
A1  - Gupta, Sheifali
A1  - Uppal, Mudita
A1  - Anand, Divya
A1  - Ortega-Mansilla, Arturo
A1  - Alharithi, Fahd S.
A1  - Almotiri, Jasem
A1  - Goyal, Nitin
JF  - Symmetry
AV  - public
VL  - 14
ER  -