Query: marine debris segmentation

title: Applying segmentation and neural networks to detect and quantify marine debris from aerial images captured by unmanned aerial system and mobile device

title: TrashCan: A Semantically Segmented Dataset towards Visual Detection of Marine Debris
abstract: This paper presents TrashCan, a large dataset comprised of images of underwater trash collected from a variety of sources, annotated both using bounding boxes and segmentation labels, for development of robust detectors of marine debris. The dataset has two versions, TrashCan Material and TrashCan Instance, corresponding to different object class configurations. The eventual goal is to develop efficient and accurate trash detection methods suitable for onboard robot deployment. Along with information about the construction and sourcing of the TrashCan dataset, we present initial results of instance segmentation from Mask R CNN and object detection from Faster R CNN. These do not represent the best possible detection results but provides an initial baseline for future work in instance segmentation and object detection on the TrashCan dataset.

title: Detecting Waterborne Debris with Sim2Real and Randomization
abstract: From palpable marine debris to microplastics, marine debris pollution has been a perennial problem. In recent years, there is an emergence of largescale clean up efforts making its way around the world. Complimentary to large scale clean up efforts, there is a nascent area in the use of unmanned and remote vehicles for detecting and removing debris. In this project, our focus is on marine debris detection where we propose to train a waterborne debris detector based on a mixture of real and synthetic training samples. We leverage on the capabilities of game engines to generate a variety of distributions and types of marine debris with variations in camera angles and distance from objects. The use of the game engine allows us to get segmentation masks at no expense as the ground truth is known. We further augment the images via domain randomization and mix with the real dataset. All our datasets and model implementations will be publicly available.

title: Review on on line visual ferrographic analysis system for marine diesel engine
abstract: Since the traditional ferrographical analysis technology has its own limitations, the research on a new on line visual ferrographical analysis system becomes very important and necessary. Firstly, the paper reviewed the current research on the on line visual system, and discussed three bottleneck problems about the system. They are the control of debris depositing, debris feature extraction and the segmentation of ferrographical images. Afterwards, several essential required functional attributes in an intact on line visual ferrographical analysis system were discussed from a technical point of view. Finally, to solve these bottlenecks, several research directions were proposed, especially on the control of debris depositing and debris segmentation for ferrographical images.

title: Detection and identification of plastics using SWIR hyperspectral imaging
abstract: Most plastics are typically transparent in the visible spectral range, rendering them challenging to detect using silicon based vision sensors. In this work a SWIR hyperspectral imaging system is used to collect the SWIR hyperspectral signatures as well as spatial information of a variety of plastics outdoors to test this technology for plastic debris detection and identification in future marine and environmental applications. In this study, hyperspectral imaging data have been collected from plastic samples including CPVC, PVC, LDPE, HDPE, PEEK PETG, PC, PP, PS, and Polyester in a natural environment. The data is acquired using a SWIR hyperspectral imaging system sensitive to 900 1700 nm wavelength range. Four spectral indices based on labeled spectral signatures have been identified and used as features to separate plastic materials and for classification of pixels. Semantic segmentation based on plastic materials is achieved in an independent scene with multiple plastic samples using shortest Euclidean distance to labeled feature cluster centers through multi variate data analysis. The results show the capability of this technology and technique to detect and classify different plastics in natural environments under different light conditions.

title: Towards Optimal Ship Navigation Using Image Processing
abstract: Shipping transportation developed over years with the technological advancements. Modern ship navigation is conducted with the help of Automatic Radar Plotting Aid (ARPA) and Electronic Chart Display and Information System (ECDIS) Location map, marine traffic, geographical conditions, and obstacles in a region can be monitored by these technologies. The obstacles may vary from icebergs and ice blocks to islands, debris, rocks, or other vessels in a given vicinity. In this study, we propose an approach for route optimization using twodimensional radar images and image segmentation in an environment with obstacles. The navigation algorithm takes image segmentation results as an input and finds the optimal route (i.e. safest and shortest) One of the advantages of this study is that the obstacles are not solely polygonal, but they may be in any shape, size, and color. The proposed approach has some practical and computational limitations; however, the future unmanned vessels could benefit from the improved applications of this route optimization approach in terms of energy consumption, time, and workforce.

title: Diffraction patterns as a tool to recognize copepods
abstract: Photographs of three species of calanoid copepods were digitized, binarized and edited to remove the debris present in the images and to simulate different degrees of segmentation. Twentyeight segmented images of the three species were generated after pre processing to obtain their digital Fourier transform or diffraction pattern. Ouster analysis was carried out for the images of the copepods and for their diffraction patterns to determine which set of images discriminated genus, species and sex of the selected organisms. The dendrograms of the diffraction patterns had more power of discrimination and 50% less variability. This method is promising, but more research is necessary in order to implement an automated system for plankton identification in the near future.  Even for the most competent plankton taxonomist, the correct identification of a marine microorganism can take a long time (Ortner et ai, 1979; Omori and Ikeda, 1984) In recent years, there has been a decrease in the number of competent plankton taxonomists (Simpson et ai, 1992) which could be the reason for some of the reported synonymies for calanoid copepods. Bjornberg (1981) reports that Acartia lilljeborgii is the same as Acartia denticomis and Nannocalanus minor is the same as Calanus vulgus, and Trujillo Ortfz (1990) reports that Acartia californiensis is the same as Acartia bacorehuisensis. A broad group of researchers have been trying to implement automated systems in order to identify and count organisms in a faster and more accurate way. These systems include the Coulter Counter (Sheldon and Parsons, 1967) the in situ Coulter Counter (Herman and Dauphinee, 1980; Herman and Mitchell, 1981) silhouette photography (Ortner, 1979) and the video camera (Liacopoulos, 1983; Latrous, 1984; Rolke and Lenz, 1984) However, none of these systems ensure complete effectiveness of the taxonomic classification due to the orientation of the organism or the resolution of the image. Another group of researchers have begun to use coherent optical processing for the recognition of planktonic organisms (Cairns et al. 1972; Almeida et al. 1976; Jeffries et al. 1980,1984) A very important part of these techniques is the acquisition of a Fourier transform or diffraction pattern of the organism. The rapid development of computational sciences has made image processing software available to almost everyone in order to obtain diffraction patterns and other image processing tools. Coherent optical processing techniques were introduced by Vander Lugt (1964) The objects they used to test their systems were images of O Oxford University Press 1471 V.A.Zavala Hamz el al. SELECTION OF ORGANISMS AND PHOTOGRAPHS DIGITAL BINARY CONTOURS AND SCANNING DIGITAL DIFFRACTION PATTERNS CORRELATIONS AND DENDROGRAMS OF ORGANISMS AND DIFFRACTION PATTERNS Complate Linkage

title: Quantitative overview of marine debris ingested by marine megafauna.
abstract: This review quantifies plastic interaction in marine biota. Firstly, entanglement and ingestion records for all marine birds, mammals, turtles, fish, and invertebrate species, are summarized from 747 studies. Marine debris affected 914 species through entanglement and/or ingestion. Ingestion was recorded for 701 species, entanglement was documented for 354 species. Secondly, the frequency of occurrence of ingestion per species (Sp %FO) was extracted for marine birds, mammals and turtles. Thirdly, for seabird species, average numbers of plastics ingested per individual were determined. Highest Sp %FO and average number of plastics were found in tubenosed seabirds with 41% of all birds analysed having plastics, on average 9.9 particles per bird. The Sp %FO and average number of ingested particles is lower for most other species. However, for certain species, ingestion rates of litter are reason for serious concern. Standardized methods are crucial for future studies, to generate datasets that allow higher level ecosystem analyses.

title: Ranking environmental degradation trends of plastic marine debris based on physical properties and molecular structure
abstract: As plastic marine debris continues to accumulate in the oceans, many important questions surround this global dilemma. In particular, how many descriptors would be necessary to model the degradation behavior of ocean plastics or understand if degradation is possible? Here, we report a data driven approach to elucidate degradation trends of plastic debris by linking abiotic and biotic degradation behavior in seawater with physical properties and molecular structures. The results reveal a hierarchy of predictors to quantify surface erosion as well as combinations of features, like glass transition temperature and hydrophobicity, to classify ocean plastics into fast, medium, and slow degradation categories. Furthermore, to account for weathering and environmental factors, two equations model the influence of seawater temperature and mechanical forces. Accumulation of micro and nano plastic in the oceans has emerged as a global challenge. Here, the authors predict a hierarchy of features that regulate their degradation and surface erosion by a thorough analysis of polymer structure, composition, physical properties and degradation data.

title: The physical oceanography of the transport of floating marine debris
abstract: Marine plastic debris floating on the ocean surface is a major environmental problem. However, its distribution in the ocean is poorly mapped, and most of the plastic waste estimated to have entered the ocean from land is unaccounted for. Better understanding of how plastic debris is transported from coastal and marine sources is crucial to quantify and close the global inventory of marine plastics, which in turn represents critical information for mitigation or policy strategies. At the same time, plastic is a unique tracer that provides an opportunity to learn more about the physics and dynamics of our ocean across multiple scales, from the Ekman convergence in basin scale gyres to individual waves in the surfzone. In this review, we comprehensively discuss what is known about the different processes that govern the transport of floating marine plastic debris in both the open ocean and the coastal zones, based on the published literature and referring to insights from neighbouring fields such as oil spill dispersion, marine safety recovery, plankton connectivity, and others. We discuss how measurements of marine plastics (both in situ and in the laboratory) remote sensing, and numerical simulations can elucidate these processes and their interactions across spatio temporal scales.