eprintid: 3715 rev_number: 8 eprint_status: archive userid: 2 dir: disk0/00/00/37/15 datestamp: 2022-09-29 03:03:28 lastmod: 2022-09-29 03:03:29 status_changed: 2022-09-29 03:03:28 type: article metadata_visibility: show creators_name: Verma, Vishal creators_name: Gupta, Deepali creators_name: Gupta, Sheifali creators_name: Uppal, Mudita creators_name: Anand, Divya creators_name: Ortega-Mansilla, Arturo creators_name: Alharithi, Fahd S. creators_name: Almotiri, Jasem creators_name: Goyal, Nitin creators_id: creators_id: creators_id: creators_id: creators_id: divya.anand@uneatlantico.es creators_id: arturo.ortega@uneatlantico.es creators_id: creators_id: creators_id: title: A Deep Learning-Based Intelligent Garbage Detection System Using an Unmanned Aerial Vehicle ispublished: pub subjects: uneat_eng divisions: uninimx_produccion_cientifica full_text_status: public keywords: convolutional neural network; data augmentation; deep learning; garbage image symmetry; unmanned aerial vehicle abstract: 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 date: 2022 publication: Symmetry volume: 14 number: 5 pagerange: 960 id_number: doi:10.3390/sym14050960 refereed: TRUE issn: 2073-8994 official_url: http://doi.org/10.3390/sym14050960 access: open language: en citation: Artículo Materias > Ingeniería Universidad Internacional Iberoamericana México > Investigación > Producción Científica Abierto Inglés 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 metadata Verma, Vishal; Gupta, Deepali; Gupta, Sheifali; Uppal, Mudita; Anand, Divya; Ortega-Mansilla, Arturo; Alharithi, Fahd S.; Almotiri, Jasem y Goyal, Nitin mail SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, divya.anand@uneatlantico.es, arturo.ortega@uneatlantico.es, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR (2022) A Deep Learning-Based Intelligent Garbage Detection System Using an Unmanned Aerial Vehicle. Symmetry, 14 (5). p. 960. ISSN 2073-8994 document_url: http://repositorio.unini.edu.mx/id/eprint/3715/1/symmetry-14-00960-v2.pdf