2020 5th International Conference on Smart and Sustainable Technologies (SpliTech) 2020
DOI: 10.23919/splitech49282.2020.9243709
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Detecting Underwater Sea Litter Using Deep Neural Networks: An Initial Study

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Cited by 8 publications
(9 citation statements)
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“…Table 7 gives an overview of results presented in the literature using different network architectures. While [27][28][29] use realistic underwater images, [23] uses forward-looking sonar (FLS) imagery with constructed 96 × 96 image crops of debris objects utilized for classifier's training and testing. In terms of accuracy, classifcation model from [23] gives better results than similar models trained and validated on underwater RGB images that do not contain only one centered debris object, as expected.…”
Section: Discussionmentioning
confidence: 99%
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“…Table 7 gives an overview of results presented in the literature using different network architectures. While [27][28][29] use realistic underwater images, [23] uses forward-looking sonar (FLS) imagery with constructed 96 × 96 image crops of debris objects utilized for classifier's training and testing. In terms of accuracy, classifcation model from [23] gives better results than similar models trained and validated on underwater RGB images that do not contain only one centered debris object, as expected.…”
Section: Discussionmentioning
confidence: 99%
“…Politikos et al [28] use region-based CNN for the automated detection of seafloor marine litter on imaginary acquired in Ermoupolis bay, Syros Island, Greece. Musić et al [29] carried an initial study out on the performance of neural networks for the detection and classification of underwater sea litter images trained and tested on a dataset built using images available from the Internet and hybrid images generated using a Blender environment from given background images and 3D litter models. In [30], machine learning techniques, including CNNs, are utilized to automatically classify images of five types of microplastic particles present on beaches in the Canary Islands.…”
Section: Introductionmentioning
confidence: 99%
“…Five of these studies (Bajaj et al, 2021;Fulton et al, 2019;Marin et al, 2021;Xue et al, 2021bXue et al, , 2021a extracted data from the deep-sea debris database 2 provided by Japan Agency for Marine-earth Science and Technology. Four studies (Hegde et al, 2021;Marin et al, 2021;Musić et al, 2020;Wu et al, 2020) retrieved images from internet. While authors of these studies had to manually produce the annotations, one study (Deng et al, 2021) directly utilized the images with annotations from the TrashCan dataset (Hong et al, 2020).…”
Section: Employed Dataset Sourcesmentioning
confidence: 99%
“…Gathering a balanced dataset with accurate labels becomes challenging as the number of classes increases. We identify 9 studies (Marin et al, 2021;Martin et al, 2021;Musić et al, 2020;Politikos et al, 2021;Tharani et al, 2021;Thiagarajan and Satheesh Kumar, 2019;Tian et al, 2021;Wolf et al, 2020;Xue et al, 2021b) working with unbalanced datasets, featuring classes with very scarce data (e.g., shoes, plastic cups, string and cord). Depending on the sensor used and its resolution, small objects (e.g., straws, toothpicks, and cotton buds) may be far less visible than others (Tharani et al, 2021).…”
Section: Dataset Labelsmentioning
confidence: 99%
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