Coastal Waste Detection Based on Deep Convolutional Neural Networks

Coastal waste not only has a seriously destructive effect on human life and marine ecosystems, but it also poses a long-term economic and environmental threat. To solve the issues of a poor manual coastal waste sorting environment, such as low sorting efficiency and heavy tasks, we develop a novel d...

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Autores principales:	Chengjuan Ren, Hyunjun Jung, Sukhoon Lee, Dongwon Jeong
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	coastal waste Faster R-CNN deep convolutional neural network environmental threat Chemical technology TP1-1185
Acceso en línea:	https://doaj.org/article/f810585b9c4146a4a39aa44425bcf90a
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spelling	oai:doaj.org-article:f810585b9c4146a4a39aa44425bcf90a2021-11-11T19:13:50ZCoastal Waste Detection Based on Deep Convolutional Neural Networks10.3390/s212172691424-8220https://doaj.org/article/f810585b9c4146a4a39aa44425bcf90a2021-10-01T00:00:00Zhttps://www.mdpi.com/1424-8220/21/21/7269https://doaj.org/toc/1424-8220Coastal waste not only has a seriously destructive effect on human life and marine ecosystems, but it also poses a long-term economic and environmental threat. To solve the issues of a poor manual coastal waste sorting environment, such as low sorting efficiency and heavy tasks, we develop a novel deep convolutional neural network by combining several strategies to realize intelligent waste recognition and classification based on the state-of-the-art Faster R-CNN framework. Firstly, to effectively detect small objects, we consider multiple-scale fusion to get rich semantic information from the shallower feature map. Secondly, RoI Align is introduced to solve positioning deviation caused by the regions of interest pooling. Moreover, it is necessary to correct key parameters and take on data augmentation to improve model performance. Besides, we create a new waste object dataset, named IST-Waste, which is made publicly to facilitate future research in this field. As a consequence, the experiment shows that the algorithm’s mAP reaches 83%. Detection performance is significantly better than Faster R-CNN and SSD. Thus, the developed scheme achieves higher accuracy and better performance against the state-of-the-art alternative.Chengjuan RenHyunjun JungSukhoon LeeDongwon JeongMDPI AGarticlecoastal wasteFaster R-CNNdeep convolutional neural networkenvironmental threatChemical technologyTP1-1185ENSensors, Vol 21, Iss 7269, p 7269 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	coastal waste Faster R-CNN deep convolutional neural network environmental threat Chemical technology TP1-1185
spellingShingle	coastal waste Faster R-CNN deep convolutional neural network environmental threat Chemical technology TP1-1185 Chengjuan Ren Hyunjun Jung Sukhoon Lee Dongwon Jeong Coastal Waste Detection Based on Deep Convolutional Neural Networks
description	Coastal waste not only has a seriously destructive effect on human life and marine ecosystems, but it also poses a long-term economic and environmental threat. To solve the issues of a poor manual coastal waste sorting environment, such as low sorting efficiency and heavy tasks, we develop a novel deep convolutional neural network by combining several strategies to realize intelligent waste recognition and classification based on the state-of-the-art Faster R-CNN framework. Firstly, to effectively detect small objects, we consider multiple-scale fusion to get rich semantic information from the shallower feature map. Secondly, RoI Align is introduced to solve positioning deviation caused by the regions of interest pooling. Moreover, it is necessary to correct key parameters and take on data augmentation to improve model performance. Besides, we create a new waste object dataset, named IST-Waste, which is made publicly to facilitate future research in this field. As a consequence, the experiment shows that the algorithm’s mAP reaches 83%. Detection performance is significantly better than Faster R-CNN and SSD. Thus, the developed scheme achieves higher accuracy and better performance against the state-of-the-art alternative.
format	article
author	Chengjuan Ren Hyunjun Jung Sukhoon Lee Dongwon Jeong
author_facet	Chengjuan Ren Hyunjun Jung Sukhoon Lee Dongwon Jeong
author_sort	Chengjuan Ren
title	Coastal Waste Detection Based on Deep Convolutional Neural Networks
title_short	Coastal Waste Detection Based on Deep Convolutional Neural Networks
title_full	Coastal Waste Detection Based on Deep Convolutional Neural Networks
title_fullStr	Coastal Waste Detection Based on Deep Convolutional Neural Networks
title_full_unstemmed	Coastal Waste Detection Based on Deep Convolutional Neural Networks
title_sort	coastal waste detection based on deep convolutional neural networks
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/f810585b9c4146a4a39aa44425bcf90a
work_keys_str_mv	AT chengjuanren coastalwastedetectionbasedondeepconvolutionalneuralnetworks AT hyunjunjung coastalwastedetectionbasedondeepconvolutionalneuralnetworks AT sukhoonlee coastalwastedetectionbasedondeepconvolutionalneuralnetworks AT dongwonjeong coastalwastedetectionbasedondeepconvolutionalneuralnetworks
_version_	1718431621477040128

Coastal Waste Detection Based on Deep Convolutional Neural Networks

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