Random Forest-Based River Plastic Detection With a Handheld Multispectral Camera

Cortesi, I.; Masiero, Andrea; Giglio, Michaela De; Tucci, Grazia; Dubbini, Marco

doi:10.5194/isprs-archives-xliii-b1-2021-9-2021

Cited by 7 publications

(5 citation statements)

References 16 publications

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“…To improve the accuracy of the RF model, hyperparameters were tuned to optimize the model's performance. 63 Specifically, the number of estimators, which is the number of trees in a given forest, was set to a value of 100. Gini criterion was used to measure the quality of split.…”

Section: Resultsmentioning

confidence: 99%

Mid-infrared spectroscopy and machine learning for postconsumer plastics recycling

Stavinski

Maheshkar

Thomas

et al. 2023

Environ. Sci.: Adv.

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Section: Resultsmentioning

confidence: 99%

Mid-infrared spectroscopy and machine learning for postconsumer plastics recycling

Stavinski

Maheshkar

Thomas

et al. 2023

Environ. Sci.: Adv.

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“…Ideally, a unique classifier, as in (Cortesi et al, 2021), able to properly distinguish "Plastic" from "Other" pixels would be preferable. However, according to the experimental tests performed with the collected data (see Table 2), the performance of such a in certain cases is unsatisfying.…”

Section: Methodsmentioning

confidence: 99%

Uav-Based River Plastic Detection With a Multispectral Camera

Cortesi

Masiero²,

Tucci³

et al. 2022

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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Abstract. Plastic is the third world’s most produced material by industry (after concrete and steel), but people recycle only 9% of plastic that they have used. The other parts are either burned or accumulated in landfills and in the environment, the latter being the cause of many serious consequences, in particular when considering a long-term scenario. A significant part the plastic waste is dispersed in the aquatic environment, having a dramatic impact on the aquatic flora and fauna. This motivated several works aiming at the development of methodologies and automatic or semi-automatic tools for the plastic pollution detection, in order to enable and facilitate its recovery. This paper deals with the problem of plastic waste automatic detection in the fluvial and aquatic environment. The goal is that of exploiting the well-recognized potential of machine learning tools in object detection applications. A machine learning tool, based on random forest classifiers, has been developed to properly detect plastic objects in multi-spectral imagery collected by an unmanned aerial vehicle (UAV). In the developed approach, the outcome is determined by the combination of two random forest classifiers and of an area-based selection criterion. The approach is tested on 154 images collected by a multi-spectral proximity sensor, namely the MAIA-S2 camera, in a fluvial environment, on the Arno river (Italy), where an artificial controlled scenario was created by introducing plastic samples anchored to the ground. The obtained results are quite satisfactory in terms of object detection accuracy and recall (both higher than 98%), while presenting a remarkably lower performance in terms of precision and quality. The overall performance appears also to be dependent on the UAV flight altitude, being worse at higher altitudes, as expected.

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“…Multi-spectral imagery has also been successfully used for plastic detection in the satellite case (e.g. for plastic detection on seas and oceans (Biermann et al, 2020, Themistocleous et al, 2020), and, more recently, on the rivers thanks to UAV-imagery (Cortesi et al, 2022, Cortesi et al, 2021, Gonc ¸alves and Andriolo, 2022, Maharjan et al, 2022, Cheng et al, 2021.…”

Section: Introductionmentioning

confidence: 99%

Mini Uav-Based Litter Detection on River Banks

Cortesi

Mugnai²,

Angelini³

et al. 2023

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

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Abstract. Most of the anthropic pollution arriving to seas and oceans is carried by rivers, leading to a dramatic impact on the aquatic flora and fauna. Hence, several strategies have already been considered to reduce the use of plastic (and non biodegradable) objects, fostering recycling, detect litter in the environment, and catch it. This work aims at implementing a litter detection approach usable also in urban areas, hence considering a mini-UAV (Unmanned Aerial Vehicle) in order to reduce the issues related to flight restrictions. The implemented strategy exploits a high resolution map of the area of interest, a properly trained deep learning litter object detector, and a vision based localization system, which allows to remarkably reduce the positioning error of the UAV navigation system, in order to provide estimates of the litter object positions with an accuracy at decimeter level for objects not too far from locations recognizable in the map.

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Random Forest-Based River Plastic Detection With a Handheld Multispectral Camera

Cited by 7 publications

References 16 publications

Mid-infrared spectroscopy and machine learning for postconsumer plastics recycling

Mid-infrared spectroscopy and machine learning for postconsumer plastics recycling

Uav-Based River Plastic Detection With a Multispectral Camera

Mini Uav-Based Litter Detection on River Banks

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