A new approach to oil spill detection that combines deep learning with unmanned aerial vehicles

Jiao, Zeyu; Jia, Guozhu; Cai, Yi

doi:10.1016/j.cie.2018.11.008

Cited by 99 publications

(45 citation statements)

References 35 publications

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“…Recent deep-learning-based methods [9][10][11][12][13][14][15][16][17][18][19][20][21][22][23][24][25][26][27][28] that used CNN structures for both automated feature extraction, as well as classification of SAR images have relied on the use of patches to reduce the background concentration in the tested images. Pre-trained models, such as ResNet 101, VGG-16, and GAN networks as in [10][11][12][13][14][15][16][17][18][19][20][21][22][23][24][25][26][27][28] or multi-level CNN networks as in [9], were introduced to classify patches with modest performance (i.e., precision and Dice score). In [23], authors obtained improved results via introducing the use of VGG-16 and dark batch generation algorithm on spaceborne SAR images.…”

Section: Discussionmentioning

confidence: 99%

“…Jiao et al introduced a pre-trained deep convolutional neural network based on VGG-16 for classifying oil spill instances and the Otsu algorithm to reduce the false positive rate and Maximally Stable Extremal Regions (MSER) algorithm for locating the oil spill by generating a detection box [16]. The VGG-16 achieved a recall of 99.5% and an f -measure of 98.5%.…”

Section: Related Workmentioning

confidence: 99%

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A Deep-Learning Framework for the Detection of Oil Spills from SAR Data

Shaban

Salim

Khalifeh

et al. 2021

Sensors

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Oil leaks onto water surfaces from big tankers, ships, and pipeline cracks cause considerable damage and harm to the marine environment. Synthetic Aperture Radar (SAR) images provide an approximate representation for target scenes, including sea and land surfaces, ships, oil spills, and look-alikes. Detection and segmentation of oil spills from SAR images are crucial to aid in leak cleanups and protecting the environment. This paper introduces a two-stage deep-learning framework for the identification of oil spill occurrences based on a highly unbalanced dataset. The first stage classifies patches based on the percentage of oil spill pixels using a novel 23-layer Convolutional Neural Network. In contrast, the second stage performs semantic segmentation using a five-stage U-Net structure. The generalized Dice loss is minimized to account for the reduced oil spill representation in the patches. The results of this study are very promising and provide a comparable improved precision and Dice score compared to related work.

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Section: Discussionmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

A Deep-Learning Framework for the Detection of Oil Spills from SAR Data

Shaban

Salim

Khalifeh

et al. 2021

Sensors

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“…In [27], it is shown that it is possible to differentiate between water, oil slicks, and interference, such as algae. In [28] a solution is presented for automated detection while using object detection based on RGB cameras mounted on a UAV. The implementation is successful, but, using only this technology, it is not possible to detect oil spills during nighttime.…”

Section: Literaturementioning

confidence: 99%

Oil Spill Detection Using Machine Learning and Infrared Images

et al. 2020

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The detection of oil spills in water is a frequently researched area, but most of the research has been based on very large patches of crude oil on offshore areas. We present a novel framework for detecting oil spills inside a port environment, while using unmanned areal vehicles (UAV) and a thermal infrared (IR) camera. This framework is split into a training part and an operational part. In the training part, we present a process for automatically annotating RGB images and matching them with the IR images in order to create a dataset. The infrared imaging camera is crucial to be able to detect oil spills during nighttime. This dataset is then used to train on a convolutional neural network (CNN). Seven different CNN segmentation architectures and eight different feature extractors are tested in order to find the best suited combination for this task. In the operational part, we propose a method to have a real-time, onboard UAV oil spill detection using the pre-trained network and a low power interference device. A controlled experiment in the port of Antwerp showed that we are able to achieve an accuracy of 89% while only using the IR camera.

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“…Due to the periodicity of satellite data acquisition, they are impossible to monitor oil spills in real-time. With the rapid development of unmanned aerial vehicle (UAV) technology, the oil spill monitoring technology of airborne sensors have boomed [9]. However, the UAV monitoring manners are still restricted by the weather and sea states, often unable to carry out the missions.…”

Section: Introductionmentioning

confidence: 99%

Oil Spill Detection Using LBP Feature and K-Means Clustering in Shipborne Radar Image

Pan

Jia

et al. 2021

JMSE

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Oil spill accidents have seriously harmed the marine environment. Effective oil spill monitoring can provide strong scientific and technological support for emergency response of law enforcement departments. Shipborne radar can be used to monitor oil spills immediately after the accident. In this paper, the original shipborne radar image collected by the teaching-practice ship Yukun of Dalian Maritime University during the oil spill accident of Dalian on 16 July 2010 was taken as the research data, and an oil spill detection method was proposed by using LBP texture feature and K-means algorithm. First, Laplacian operator, Otsu algorithm, and mean filter were used to suppress the co-frequency interference noises and high brightness pixels. Then the gray intensity correction matrix was used to reduce image nonuniformity. Next, using LBP texture feature and K-means clustering algorithm, the effective oil spill regions were extracted. Finally, the adaptive threshold was applied to identify the oil films. This method can automatically detect oil spills in shipborne radar image. It can provide a guarantee for real-time monitoring of oil spill accidents.

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A new approach to oil spill detection that combines deep learning with unmanned aerial vehicles

Cited by 99 publications

References 35 publications

A Deep-Learning Framework for the Detection of Oil Spills from SAR Data

A Deep-Learning Framework for the Detection of Oil Spills from SAR Data

Oil Spill Detection Using Machine Learning and Infrared Images

Oil Spill Detection Using LBP Feature and K-Means Clustering in Shipborne Radar Image

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