Deep Learning-Based Small Object Detection and Classification Model for Garbage Waste Management in Smart Cities and IoT Environment

In recent years, object detection has gained significant interest and is considered a challenging problem in computer vision. Object detection is mainly employed for several applications, such as instance segmentation, object tracking, image captioning, healthcare, etc. Recent studies have reported...

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Vydáno v:Applied sciences Ročník 12; číslo 5; s. 2281
Hlavní autoři: Alsubaei, Faisal S., Al-Wesabi, Fahd N., Hilal, Anwer Mustafa
Médium: Journal Article
Jazyk:angličtina
Vydáno: Basel MDPI AG 01.03.2022
Témata:
Boxes
Classification
Deep learning
Internet of Things
IoT
Methods
Neural networks
object detection
Semantics
small objects
smart cities
Teeth
ISSN:2076-3417, 2076-3417
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Shrnutí:In recent years, object detection has gained significant interest and is considered a challenging problem in computer vision. Object detection is mainly employed for several applications, such as instance segmentation, object tracking, image captioning, healthcare, etc. Recent studies have reported that deep learning (DL) models can be employed for effective object detection compared to traditional methods. The rapid urbanization of smart cities necessitates the design of intelligent and automated waste management techniques for effective recycling of waste. In this view, this study develops a novel deep learning-based small object detection and classification model for garbage waste management (DLSODC-GWM) technique. The proposed DLSODC-GWM technique mainly focuses on detecting and classifying small garbage waste objects to assist intelligent waste management systems. The DLSODC-GWM technique follows two major processes, namely, object detection and classification. For object detection, an arithmetic optimization algorithm (AOA) with an improved RefineDet (IRD) model is applied, where the hyperparameters of the IRD model are optimally chosen by the AOA. Secondly, the functional link neural network (FLNN) technique was applied for the classification of waste objects into multiple classes. The design of IRD for waste classification and AOA-based hyperparameter tuning demonstrates the novelty of the work. The performance validation of the DLSODC-GWM technique is performed using benchmark datasets, and the experimental results show the promising performance of the DLSODC-GWM method on existing approaches with a maximum accuy of 98.61%.
Bibliografie:ObjectType-Article-1
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ISSN:2076-3417
2076-3417
DOI:10.3390/app12052281