2,712 research outputs found
Sea-Surface Object Detection Based on Electro-Optical Sensors: A Review
Sea-surface object detection is critical for navigation safety of autonomous ships. Electrooptical (EO) sensors, such as video cameras, complement radar on board in detecting small obstacle
sea-surface objects. Traditionally, researchers have used horizon detection, background subtraction, and
foreground segmentation techniques to detect sea-surface objects. Recently, deep learning-based object
detection technologies have been gradually applied to sea-surface object detection. This article demonstrates a comprehensive overview of sea-surface object-detection approaches where the advantages
and drawbacks of each technique are compared, covering four essential aspects: EO sensors and image
types, traditional object-detection methods, deep learning methods, and maritime datasets collection. In
particular, sea-surface object detections based on deep learning methods are thoroughly analyzed and
compared with highly influential public datasets introduced as benchmarks to verify the effectiveness of
these approaches. The arti
Ship recognition on the sea surface using aerial images taken by Uav : a deep learning approach
Dissertation submitted in partial fulfilment of the requirements for the Degree of Master of Science in Geospatial TechnologiesOceans are very important for mankind, because they are a very important source of
food, they have a very large impact on the global environmental equilibrium, and it is
over the oceans that most of the world commerce is done. Thus, maritime surveillance
and monitoring, in particular identifying the ships used, is of great importance to
oversee activities like fishing, marine transportation, navigation in general, illegal
border encroachment, and search and rescue operations. In this thesis, we used images
obtained with Unmanned Aerial Vehicles (UAVs) over the Atlantic Ocean to identify
what type of ship (if any) is present in a given location. Images generated from UAV
cameras suffer from camera motion, scale variability, variability in the sea surface and
sun glares. Extracting information from these images is challenging and is mostly done
by human operators, but advances in computer vision technology and development of
deep learning techniques in recent years have made it possible to do so automatically.
We used four of the state-of-art pretrained deep learning network models, namely
VGG16, Xception, ResNet and InceptionResNet trained on ImageNet dataset, modified
their original structure using transfer learning based fine tuning techniques and then
trained them on our dataset to create new models. We managed to achieve very high
accuracy (99.6 to 99.9% correct classifications) when classifying the ships that appear
on the images of our dataset. With such a high success rate (albeit at the cost of high
computing power), we can proceed to implement these algorithms on maritime patrol
UAVs, and thus improve Maritime Situational Awareness
Recent Trends in Video Surveillance System in Dense Environment: - A Review Paper
Snow, fog, lightning, torrential rain, and darkness degrade outdoor surveillance footage. The detection, categorization, and event/object recognition capabilities of video surveillance systems in congested environments have attracted considerable interest. Real-time video analysis algorithms in various weather conditions have been enhanced by technology. Other examples include background extraction, the see-through algorithm, deep learning models, CNN for nocturnal incursions, the system for high-quality underwater monitoring utilising optical-wireless video surveillance, LVENet, and edge computing. In the current study, these methodologies improved monitoring efficiency and decreased human error. This study details these video surveillance techniques, platforms, and supplementary materials. After discussing prevalent building and architectural styles briefly, significant system evaluations are presented. This study contrasts current surveillance systems with various methods for real-time video processing under challenging weather conditions in order to provide readers with a thorough understanding of the system. The following research is also highlighted
Research on Ship Classification using Faster Region Convolutional Neural Network for Port Security
Huvudsyftet med studien var att se i vilken grad det gÄr att finna samarbeten genom material- och/eller energiutbyten mellan nÀrliggande anlÀggningar inom skogsindustrin i Sverige. Genom att göra en inventering av vilka anlÀggningar som finns inom skogsindustrin och sedan kontakta dessa, sammanstÀlldes en lista över de olika anlÀggningarna och deras olika samarbeten. Inventeringen gjordes med hjÀlp av olika branschorganisationer samt sökmotorer pÄ Internet. Utöver detta besöktes ocksÄ fyra intressanta fall för att ge en inblick i hur dessa samarbeten kan se ut. Studien visar pÄ att den hÀr typen av samarbeten existerar inom skogsindustrin och att drygt en tredjedel av de studerade anlÀggningarna har nÄgon form av samarbeten rörande dessa frÄgor. Detta pekar pÄ att man inom skogsindustrin Àr lÄngt framme nÀr det gÀller resursutnyttjande och att möjligheten att minimera sin energi- och materialanvÀndning hela tiden Àr en relevant frÄga. Det finns med stor sannolikhet Ànnu fler sÄdana samarbeten som inte framkommit vid undersökningen och en intressant aspekt Àr att vid de besök som gjordes upptÀcktes samarbeten som inte uppmÀrksammats vid tidigare kontakter. Av de 152 tillfrÄgade anlÀggningarna i inventeringen erhölls svar frÄn 117 stycken vilket tyder pÄ att det finns ett stort intresse för dessa frÄgor inom skogsindustrin. Flera av de anlÀggningar som inte hade nÄgra samarbeten kring dessa frÄgor svarade ocksÄ att de hela tiden undersöker möjligheten till att inleda sÄdana. MÄnga av samarbetena rörande dessa frÄgor kretsar kring leveranser av el och Änga samt spÄn och flis men en del andra intressanta samarbeten har ocksÄ framkommit. Exempelvis anvÀnds slam frÄn bioreningsdammar till brÀnsle, jordförbÀttringsmedel och som tÀckmaterial vid deponier. Sammanfattningsvis tyder detta pÄ att skogsindustrin ligger lÄngt framme gÀllande dessa frÄgor men att det fortfarande finns mer att göra om energi- och materialanvÀndningen och dÀrigenom den negativa miljöpÄverkan ska minimeras.The aim and objective with this study was to investigate to what extent co-operation through material and energy exchange between adjacent industries among the forest industry in Sweden could be found. First, an inventory of the industries in the forest industry was conducted. Secondly, each company was contacted with questions concerning this issue. Complementary field studies of four specific cases were conducted in order to give an insight to how these co-operations may function in reality. The result of this study illustrates that co-operations among the industries exist in the forest industry sector as more than a third of the investigated industries has some kind of co-operation regarding material and energy exchange with adjacent industries. A total number of 152 industries were identified during the inventory phase and 117 of those industries participated in the study with their own answers. This high participation rate enhances the impression that these are important questions to the forest industry sector. Numerous of the co-operations mentioned revolve around electricity, steam, and by products from sawmills, like woodchips and sawdust. Nevertheless, a few other interesting co-operations have also been revealed during the study, for example; sludge from some of the pulp mills are used as fuel, soil fertilizer and as covering material at landfills. An interesting point is that co-operations, which not were discovered during the earlier correspondence with the industries, in fact were revealed during the field studies. Therefore, the probability that there are more existing co-operations between adjacent industries than the findings in the study reveals, are high. To sum up, this shows that the forest industry is well in advance regarding co-operation through material and energy exchange between adjacent industries. However, there is still a lot to be done if the negative effect on the environment from the forest industry should be minimised
Combining computer game-based behavioural experiments with high-density EEG and infrared gaze tracking
Rigorous, quantitative examination of therapeutic techniques anecdotally reported to have been successful in people with autism who lack communicative speech will help guide basic science toward a more complete characterisation of the cognitive profile in this underserved subpopulation, and show the extent to which theories and results developed with the high-functioning subpopulation may apply. This study examines a novel therapy, the "Rapid Prompting Method" (RPM). RPM is a parent-developed communicative and educational therapy for persons with autism who do not speak or who have difficulty using speech communicatively.The technique aims to develop a means of interactive learning by pointing amongst multiple-choice options presented at different locations in space, with the aid of sensory "prompts" which evoke a response without cueing any specific response option. The prompts are meant to draw and to maintain attention to the communicative taskâmaking the communicative and educational content coincident with the most physically salient, attention-capturing stimulus â and to extinguish the sensoryâmotor preoccupations with which the prompts compete.ideo-recorded RPM sessions with nine autistic children ages 8â14years who lacked functional communicative speech were coded for behaviours of interest
Assessing High Dynamic Range Imagery Performance for Object Detection in Maritime Environments
The field of autonomous robotics has benefited from the implementation of convolutional neural networks in vision-based situational awareness. These strategies help identify surface obstacles and nearby vessels. This study proposes the introduction of high dynamic range cameras on autonomous surface vessels because these cameras capture images at different levels of exposure revealing more detail than fixed exposure cameras. To see if this introduction will be beneficial for autonomous vessels this research will create a dataset of labeled high dynamic range images and single exposure images, then train object detection networks with these datasets to compare the performance of these networks. Faster-RCNN, SSD, and YOLOv5 were used to compare. Results determined Faster-RCNN and YOLOv5 networks trained on fixed exposure images outperformed their HDR counterparts while SSDs performed better when using HDR images. Better fixed exposure network performance is likely attributed to better feature extraction for fixed exposure images. Despite performance metrics, HDR images prove more beneficial in cases of extreme light exposure since features are not lost
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