A Mobile Based Waste Classification Using MobileNets-V1 Architecture

Nurahmadan, Irzan Fajari; Arjuna, Roihan Mufli; Prasetyo, Herlambang Dwi; Hogantara, Pandu Ananto; Isnainiyah, Ika Nurlaili; Wirawan, Rio

doi:10.1109/icimcis53775.2021.9699161

Cited by 4 publications

(2 citation statements)

References 8 publications

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Section: Methodsmentioning

confidence: 99%

“…The classification model is the result of training a specific CNN model on a dataset of labeled images. Several state-ofthe-art convolutional neural network methods is tested in this research, which included Inception V3 [12], MobileNet V2 [13], Inception Resnet V2 [14], Resnet 50 [15], Mobile Net [16], and Xception [17]. The model is then converted into TensorFlow Lite model as they are highly optimized, efficient, and versatile, making them ideal for running real-time predictions on mobile [18].…”

Section: A System Architecturementioning

confidence: 99%

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MWaste: A Deep Learning Approach to Manage Household Waste

Kunwar¹

2023

Preprint

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Computer vision methods have shown to be effective in classifying garbage into recycling categories for waste processing, existing methods are costly, imprecise, and unclear. To tackle this issue, we introduce MWaste, a mobile application that uses computer vision and deep learning techniques to classify waste materials as trash, plastic, paper, metal, glass or cardboard. Its effectiveness was tested on various neural network architectures and real world images, achieving an average precision of 92% on the test set. This app can help combat climate change by enabling efficient waste processing and reducing the generation of greenhouse gases caused by incorrect waste disposal.

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