Automatic detection of seafloor marine litter using towed camera images and deep learning

Politikos, Dimitris V.; Fakiris, Elias; Davvetas, Athanasios; Klampanos, Iraklis A.; Papatheodorou, George

doi:10.1016/j.marpolbul.2021.111974

Cited by 63 publications

(24 citation statements)

References 23 publications

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“…Several works are focused on the Internet of Things (IoT), where sensors are used to monitor city cleanliness [ 11 , 12 , 13 ]. Researchers have also focused efforts on image-based approaches for marine debris, plastic and even microplastic identification [ 14 , 15 , 16 , 17 , 18 , 19 ]. However, image acquisition procedures rely on microscopes, webcams, and even aerial surveys, which are costly and often unavailable to volunteers and citizen scientists.…”

Section: Introductionmentioning

confidence: 99%

Litter Detection with Deep Learning: A Comparative Study

Córdova

Pinto

Hellevik

et al. 2022

Sensors

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Pollution in the form of litter in the natural environment is one of the great challenges of our times. Automated litter detection can help assess waste occurrences in the environment. Different machine learning solutions have been explored to develop litter detection tools, thereby supporting research, citizen science, and volunteer clean-up initiatives. However, to the best of our knowledge, no work has investigated the performance of state-of-the-art deep learning object detection approaches in the context of litter detection. In particular, no studies have focused on the assessment of those methods aiming their use in devices with low processing capabilities, e.g., mobile phones, typically employed in citizen science activities. In this paper, we fill this literature gap. We performed a comparative study involving state-of-the-art CNN architectures (e.g., Faster RCNN, Mask-RCNN, EfficientDet, RetinaNet and YOLO-v5), two litter image datasets and a smartphone. We also introduce a new dataset for litter detection, named PlastOPol, composed of 2418 images and 5300 annotations. The experimental results demonstrate that object detectors based on the YOLO family are promising for the construction of litter detection solutions, with superior performance in terms of detection accuracy, processing time, and memory footprint.

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Section: Introductionmentioning

confidence: 99%

Litter Detection with Deep Learning: A Comparative Study

Córdova

Pinto

Hellevik

et al. 2022

Sensors

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“…At this stage, with the development of deep learning, the field of marine science is also gradually using this emerging technology. Especially in this application of marine environmental monitoring, numerous scholars have used machine learning and neural network approaches to automatically detect seabed litter [ 37 , 38 , 39 ].…”

Section: Conclusion and Outlooksmentioning

confidence: 99%

An Embeddable Algorithm for Automatic Garbage Detection Based on Complex Marine Environment

Deng

Ergu

Liu

et al. 2021

Sensors

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With the continuous development of artificial intelligence, embedding object detection algorithms into autonomous underwater detectors for marine garbage cleanup has become an emerging application area. Considering the complexity of the marine environment and the low resolution of the images taken by underwater detectors, this paper proposes an improved algorithm based on Mask R-CNN, with the aim of achieving high accuracy marine garbage detection and instance segmentation. First, the idea of dilated convolution is introduced in the Feature Pyramid Network to enhance feature extraction ability for small objects. Secondly, the spatial-channel attention mechanism is used to make features learn adaptively. It can effectively focus attention on detection objects. Third, the re-scoring branch is added to improve the accuracy of instance segmentation by scoring the predicted masks based on the method of Generalized Intersection over Union. Finally, we train the proposed algorithm in this paper on the Transcan dataset, evaluating its effectiveness by various metrics and comparing it with existing algorithms. The experimental results show that compared to the baseline provided by the Transcan dataset, the algorithm in this paper improves the mAP indexes on the two tasks of garbage detection and instance segmentation by 9.6 and 5.0, respectively, which significantly improves the algorithm performance. Thus, it can be better applied in the marine environment and achieve high precision object detection and instance segmentation.

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“…In particular, earth observation data from public and commercial satellite programs [10][11][12][13][14] have been employed for detecting and monitoring Marine Debris, as well as remote sensing data from manned aircraft [15], unmanned aerial vehicles (UAVs) [16][17][18][19][20], bridgemounted [21] and underwater-cameras [22]. Spectral indices have also been proposed to enhance the detection of Marine Debris on multispectral satellite data, like the Floating Debris Index (FDI) [13] and the Plastic Index (PI) [23] that have been developed based on artificial plastic targets.…”

Section: Introductionmentioning

confidence: 99%

MARIDA: A benchmark for Marine Debris detection from Sentinel-2 remote sensing data

et al. 2022

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Currently, a significant amount of research is focused on detecting Marine Debris and assessing its spectral behaviour via remote sensing, ultimately aiming at new operational monitoring solutions. Here, we introduce a Marine Debris Archive (MARIDA), as a benchmark dataset for developing and evaluating Machine Learning (ML) algorithms capable of detecting Marine Debris. MARIDA is the first dataset based on the multispectral Sentinel-2 (S2) satellite data, which distinguishes Marine Debris from various marine features that co-exist, including Sargassum macroalgae, Ships, Natural Organic Material, Waves, Wakes, Foam, dissimilar water types (i.e., Clear, Turbid Water, Sediment-Laden Water, Shallow Water), and Clouds. We provide annotations (georeferenced polygons/ pixels) from verified plastic debris events in several geographical regions globally, during different seasons, years and sea state conditions. A detailed spectral and statistical analysis of the MARIDA dataset is presented along with well-established ML baselines for weakly supervised semantic segmentation and multi-label classification tasks. MARIDA is an open-access dataset which enables the research community to explore the spectral behaviour of certain floating materials, sea state features and water types, to develop and evaluate Marine Debris detection solutions based on artificial intelligence and deep learning architectures, as well as satellite pre-processing pipelines.

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Automatic detection of seafloor marine litter using towed camera images and deep learning

Cited by 63 publications

References 23 publications

Litter Detection with Deep Learning: A Comparative Study

Litter Detection with Deep Learning: A Comparative Study

An Embeddable Algorithm for Automatic Garbage Detection Based on Complex Marine Environment

MARIDA: A benchmark for Marine Debris detection from Sentinel-2 remote sensing data

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