scholarly journals Quantifying Floating Plastic Debris at Sea Using Vessel-Based Optical Data and Artificial Intelligence

2021 ◽  
Vol 13 (17) ◽  
pp. 3401
Author(s):  
Robin de Vries ◽  
Matthias Egger ◽  
Thomas Mani ◽  
Laurent Lebreton
Keyword(s):  
Artificial Intelligence ◽  
Remote Sensing ◽  
Object Detection ◽  
Detection Algorithm ◽  
Optical Data ◽  
Coastal Regions ◽  
Plastic Debris ◽  
Recent Advances ◽  
Bulk Processing

Despite recent advances in remote sensing of large accumulations of floating plastic debris, mainly in coastal regions, the quantification of individual macroplastic objects (>50 cm) remains challenging. Here, we have trained an object-detection algorithm by selecting and labeling footage of floating plastic debris recorded offshore with GPS-enabled action cameras aboard vessels of opportunity. Macroplastic numerical concentrations are estimated by combining the object detection solution with bulk processing of the optical data. Our results are consistent with macroplastic densities predicted by global plastic dispersal models, and reveal first insights into how camera recorded offshore macroplastic densities compare to micro- and mesoplastic concentrations collected with neuston trawls.

scholarly journals AF-EMS Detector: Improve the Multi-Scale Detection Performance of the Anchor-Free Detector

2021 ◽  
Vol 13 (2) ◽  
pp. 160
Author(s):  
Jiangqiao Yan ◽  
Liangjin Zhao ◽  
Wenhui Diao ◽  
Hongqi Wang ◽  
Xian Sun
Keyword(s):  
Remote Sensing ◽  
Object Detection ◽  
Feature Fusion ◽  
Detection Algorithm ◽  
Detection Performance ◽  
Natural Image ◽  
Detection Model ◽  
Multi Scale ◽  
Feature Pyramid ◽  
Scale Detection

As a precursor step for computer vision algorithms, object detection plays an important role in various practical application scenarios. With the objects to be detected becoming more complex, the problem of multi-scale object detection has attracted more and more attention, especially in the field of remote sensing detection. Early convolutional neural network detection algorithms are mostly based on artificially preset anchor-boxes to divide different regions in the image, and then obtain the prior position of the target. However, the anchor box is difficult to set reasonably and will cause a large amount of computational redundancy, which affects the generality of the detection model obtained under fixed parameters. In the past two years, anchor-free detection algorithm has achieved remarkable development in the field of detection on natural image. However, there is no sufficient research on how to deal with multi-scale detection more effectively in anchor-free framework and use these detectors on remote sensing images. In this paper, we propose a specific-attention Feature Pyramid Network (FPN) module, which is able to generate a feature pyramid, basing on the characteristics of objects with various sizes. In addition, this pyramid suits multi-scale object detection better. Besides, a scale-aware detection head is proposed which contains a multi-receptive feature fusion module and a size-based feature compensation module. The new anchor-free detector can obtain a more effective multi-scale feature expression. Experiments on challenging datasets show that our approach performs favorably against other methods in terms of the multi-scale object detection performance.

scholarly journals An Aircraft Object Detection Algorithm Based on Small Samples in Optical Remote Sensing Image

2020 ◽  
Vol 10 (17) ◽  
pp. 5778
Author(s):  
Ting Wang ◽  
Changqing Cao ◽  
Xiaodong Zeng ◽  
Zhejun Feng ◽  
Jingshi Shen ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Object Detection ◽  
Detection Algorithm ◽  
Small Samples ◽  
Optical Remote Sensing ◽  
Remote Sensing Images ◽  
Sensing Technology ◽  
Optical Remote Sensing Image ◽  
Aircraft Detection ◽  
Small Aircraft

In recent years, remote sensing technology has developed rapidly, and the ground resolution of spaceborne optical remote sensing images has reached the sub-meter range, providing a new technical means for aircraft object detection. Research on aircraft object detection based on optical remote sensing images is of great significance for military object detection and recognition. However, spaceborne optical remote sensing images are difficult to obtain and costly. Therefore, this paper proposes the aircraft detection algorithm, itcan detect aircraft objects with small samples. Firstly, this paper establishes an aircraft object dataset containing weak and small aircraft objects. Secondly, the detection algorithm has been proposed to detect weak and small aircraft objects. Thirdly, the aircraft detection algorithm has been proposed to detect multiple aircraft objects of varying sizes. There are 13,324 aircraft in the test set. According to the method proposed in this paper, the f1 score can achieve 90.44%. Therefore, the aircraft objects can be detected simply and efficiently by using the method proposed. It can effectively detect aircraft objects and improve early warning capabilities.

scholarly journals Identification and Explanation of Challenging Conditions for Camera-Based Object Detection of Automated Vehicles

Sensors ◽  
2020 ◽  
Vol 20 (13) ◽  
pp. 3699 ◽  
Author(s):  
Thomas Ponn ◽  
Thomas Kröger ◽  
Frank Diermeyer
Keyword(s):  
Artificial Intelligence ◽  
Object Detection ◽  
Influence Factors ◽  
Detection Algorithm ◽  
Detection Performance ◽  
Automated Vehicles ◽  
Detection Algorithms ◽  
Type Approval ◽  
Market Launch ◽  
First Time

For a safe market launch of automated vehicles, the risks of the overall system as well as the sub-components must be efficiently identified and evaluated. This also includes camera-based object detection using artificial intelligence algorithms. It is trivial and explainable that due to the principle of the camera, performance depends highly on the environmental conditions and can be poor, for example in heavy fog. However, there are other factors influencing the performance of camera-based object detection, which will be comprehensively investigated for the first time in this paper. Furthermore, a precise modeling of the detection performance and the explanation of individual detection results is not possible due to the artificial intelligence based algorithms used. Therefore, a modeling approach based on the investigated influence factors is proposed and the newly developed SHapley Additive exPlanations (SHAP) approach is adopted to analyze and explain the detection performance of different object detection algorithms. The results show that many influence factors such as the relative rotation of an object towards the camera or the position of an object on the image have basically the same influence on the detection performance regardless of the detection algorithm used. In particular, the revealed weaknesses of the tested object detectors can be used to derive challenging and critical scenarios for the testing and type approval of automated vehicles.

scholarly journals Charting Dynamic Areas in the Mackenzie River with RADARSAT-2, Simulated RADARSAT Constellation Mission and Optical Remote Sensing Data

2019 ◽  
Vol 11 (13) ◽  
pp. 1523
Author(s):  
René Chénier ◽  
Khalid Omari ◽  
Ryan Ahola ◽  
Mesha Sagram
Keyword(s):  
Remote Sensing ◽  
Change Detection ◽  
Information Source ◽  
Remote Sensing Data ◽  
Time Factors ◽  
Optical Data ◽  
Observation Data ◽  
Optical Remote Sensing ◽  
Coastal Regions ◽  
Mackenzie River

Mariners navigating within Canadian waters rely on Canadian Hydrographic Service (CHS) navigational charts to safely reach their destinations. To fulfil this need, CHS charts must accurately reflect the current state of Canadian coastal regions. While many coastal regions are stable, others are dynamic and require frequent updates. In order to ensure that important and potentially dangerous changes are reflected in CHS products, the organization, in partnership with the Canadian Space Agency, is exploring coastal change detection through satellite remote sensing (SRS). In this work, CHS examined a hybrid shoreline extraction approach which uses both Synthetic Aperture Radar (SAR) and optical data. The approach was applied for a section of the Mackenzie River, one of Canada’s most dynamic river systems. The approach used RADARSAT-2 imagery as its primary information source, due to its high positioning accuracy (5 m horizontal accuracy) and ability to allow for low and high water line charting. Landsat represented the primary optical data source due to its long historical record of Earth observation data. Additional sensors, such as Sentinel-2 and WorldView, were also used where a higher resolution was required. The shoreline extraction process is based on an image segmentation approach that uses both the radar and optical data. Critical information was collected using the automated approach to support chart updates, resulting in reductions to the financial, human and time factors present within the ship-based hydrographic survey techniques traditionally used for chart improvements. The results demonstrate the potential benefit of wide area SRS change detection within dynamic waterways for navigational chart improvements. The work also demonstrates that the approach developed for RADARSAT-2 could be implemented with data from the forthcoming RADARSAT Constellation Mission (RCM), which is critical to ensure project continuity.

scholarly journals Ship Detection from Optical Remote Sensing Images Using Multi-Scale Analysis and Fourier HOG Descriptor

2019 ◽  
Vol 11 (13) ◽  
pp. 1529 ◽  
Author(s):  
Chao Dong ◽  
Jinghong Liu ◽  
Fang Xu ◽  
Chenglong Liu
Keyword(s):  
Remote Sensing ◽  
Object Detection ◽  
Detection System ◽  
Detection Algorithm ◽  
Optical Remote Sensing ◽  
Scale Analysis ◽  
Ship Detection ◽  
Multi Scale ◽  
Saliency Maps ◽  
Multi Scale Analysis

Automatic ship detection by Unmanned Airborne Vehicles (UAVs) and satellites is one of the fundamental challenges in maritime research due to the variable appearances of ships and complex sea backgrounds. To address this issue, in this paper, a novel multi-level ship detection algorithm is proposed to detect various types of offshore ships more precisely and quickly under all possible imaging variations. Our object detection system consists of two phases. First, in the category-independent region proposal phase, the steerable pyramid for multi-scale analysis is performed to generate a set of saliency maps in which the candidate region pixels are assigned to high salient values. Then, the set of saliency maps is used for constructing the graph-based segmentation, which can produce more accurate candidate regions compared with the threshold segmentation. More importantly, the proposed algorithm can produce a rather smaller set of candidates in comparison with the classical sliding window object detection paradigm or the other region proposal algorithms. Second, in the target identification phase, a rotation-invariant descriptor, which combines the histogram of oriented gradients (HOG) cells and the Fourier basis together, is investigated to distinguish between ships and non-ships. Meanwhile, the main direction of the ship can also be estimated in this phase. The overall algorithm can account for large variations in scale and rotation. Experiments on optical remote sensing (ORS) images demonstrate the effectiveness and robustness of our detection system.

Detecting and Identifying Floating Plastic Debris in Coastal Waters using Sentinel-2 Earth Observation Data

2020 ◽  
Author(s):  
Lauren Biermann ◽  
Daniel Clewley ◽  
Victor Martinez-Vicente ◽  
Konstantinos Topouzelis
Keyword(s):  
Remote Sensing ◽  
Coastal Waters ◽  
European Space Agency ◽  
Ocean Surface ◽  
Marine Debris ◽  
Optical Data ◽  
Observation Data ◽  
Plastic Debris ◽  
Study Sites ◽  
Sentinel 2

<p>Satellite remote sensing is an invaluable tool for observing our earth systems. However, few studies have succeeded in applying this for detection of floating litter in the marine environment. We demonstrate that plastic debris aggregated on the ocean surface is detectable in optical data acquired by the European Space Agency (ESA) Sentinel-2 satellites. Furthermore, using an automated classification approach, we show that floating macroplastics are distinguishable from seawater, seaweed, sea foam, pumice, and driftwood.</p><p>Sentinel-2 was used to detect floating aggregations likely to include macroplastics across four study sites, namely: coastal waters of Accra (Ghana), Da Nang (Vietnam), the east coast of Scotland (UK), and the San Juan Islands (BC, Canada). Aggregations were detectable on sub-pixel scales using a Floating Debris Index (FDI), and were composed of a mix of materials including sea foam and seaweed. A probabilistic machine learning approach was then applied to assess if detected plastics could be discriminated from the natural sources of marine debris. Our automated Naïve Bayes classifier was trained using a library of pumice, seaweed, timber, sea foam and seawater detections, as well as validated macroplastics from Durban Harbour (South Africa). Across the four study sites, suspected marine plastics were classified as such with an accuracy approaching 90%. The ‘misclassified’ plastics were mostly identified as seawater, suggesting an insufficient amount of pixel was filled with materials.</p><p>Results from this study show that plastic debris aggregated on the ocean surface can be detected in optical data collected by Sentinel-2, and identified. With the aim of generating global ‘hotspot’ maps of floating plastics in coastal waters, automating this two-stage process across the Sentinel-2 archive is being progressed; however, the method would also be applicable to drones and other remote sensing platforms with similar band characteristics. To extend remote detection methods to river systems and optically complex and/or tidal coastal waters, in situ data collection across optical water types is the next key step.</p>

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