A Deep Learning-Based Intelligent Garbage Detection System Using an Unmanned Aerial Vehicle

被引:23
|
作者
Verma, Vishal [1 ]
Gupta, Deepali [1 ]
Gupta, Sheifali [1 ]
Uppal, Mudita [1 ]
Anand, Divya [2 ,3 ]
Ortega-Mansilla, Arturo [3 ,4 ]
Alharithi, Fahd S. [5 ]
Almotiri, Jasem [5 ]
Goyal, Nitin [6 ]
机构
[1] Chitkara Univ, Inst Engn & Technol, Rajpura 140401, Punjab, India
[2] Lovely Profess Univ, Dept Comp Sci & Engn, Jalandhar 144411, Punjab, India
[3] Univ Europea Atlantico, Higher Polytech Sch, C Isabel Torres 21, Santander 39011, Spain
[4] Univ Int Iberoamer, Dept Project Management, Campeche 24560, Campeche, Mexico
[5] Taif Univ, Coll Comp & Informat Technol, Dept Comp Sci, POB 11099, Taif 21944, Saudi Arabia
[6] Shri Vishwakarma Skill Univ, Comp Sci Engn Dept, Palwal 121102, Haryana, India
来源
SYMMETRY-BASEL | 2022年 / 14卷 / 05期
关键词
convolutional neural network; data augmentation; deep learning; garbage image symmetry; unmanned aerial vehicle; CLASSIFICATION; FRAMEWORK;
D O I
10.3390/sym14050960
中图分类号
O [数理科学和化学]; P [天文学、地球科学]; Q [生物科学]; N [自然科学总论];
学科分类号
07 ; 0710 ; 09 ;
摘要
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.
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页数:15
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