Deep CNN and MLP-based vision systems for algae detection in automatic inspection of underwater pipelines

Artificial neural networks, such as the multilayer perceptron (MLP), have been increasingly employed in various applications. Recently, deep neural networks, specially convolutional neural networks (CNN), have received considerable attention due to their ability to extract and represent high-level abstractions in data sets. This work describes a vision inspection system based on deep learning and computer vision algorithms for detection of algae in underwater pipelines. The proposed algorithm comprises a CNN or a MLP network, followed by a post-processing stage operating in spatial and temporal domains, employing clustering of neighboring detection positions and a region interception framebuffer. The performances of MLP, employing different descriptors, and CNN classifiers are compared in real-world scenarios. It is shown that the post-processing stage considerably decreases the number of false positives, resulting in an accuracy rate of 99.39%.Redes neurais artificiais, como o perceptron multicamada (MLP), têm sido cada vez mais empregadas em várias aplicações. Recentemente, as redes neurais profundas (deep neural networks), especialmente as redes neurais convolutivas (CNN), receberam atenção considerável devido à sua capacidade de extrair e representar abstrações de alto nível em conjuntos de dados. Esta dissertação descreve um sistema de inspeção automático baseado em algoritmos de aprendizado profundo (deep learning) e visão computacional para detecção de algas em dutos submarinos. O algoritmo proposto compreende uma rede CNN ou MLP, seguida de uma fase de pós-processamento que opera em domínios espaciais e temporais, empregando agrupamento de posições de detecção vizinhas e um buffer das regiões de interseção ao longo dos quadros. Os desempenhos de MLP, empregando diferentes descritores, e os classificadores CNN são comparados em cenários do mundo real. Mostra-se que a fase de pos-processamento diminui consideravelmente o número de falsos positivos, resultando em uma taxa de acerto de 99,39%

Automatic Hierarchical Classification of Kelps utilizing Deep Residual Feature

Across the globe, remote image data is rapidly being collected for the assessment of benthic communities from shallow to extremely deep waters on continental slopes to the abyssal seas. Exploiting this data is presently limited by the time it takes for experts to identify organisms found in these images. With this limitation in mind, a large effort has been made globally to introduce automation and machine learning algorithms to accelerate both classification and assessment of marine benthic biota. One major issue lies with organisms that move with swell and currents, like kelps. This paper presents an automatic hierarchical classification method (local binary classification as opposed to the conventional flat classification) to classify kelps in images collected by autonomous underwater vehicles. The proposed kelp classification approach exploits learned feature representations extracted from deep residual networks. We show that these generic features outperform the traditional off-the-shelf CNN features and the conventional hand-crafted features. Experiments also demonstrate that the hierarchical classification method outperforms the traditional parallel multi-class classifications by a significant margin (90.0% vs 57.6% and 77.2% vs 59.0%) on Benthoz15 and Rottnest datasets respectively. Furthermore, we compare different hierarchical classification approaches and experimentally show that the sibling hierarchical training approach outperforms the inclusive hierarchical approach by a significant margin. We also report an application of our proposed method to study the change in kelp cover over time for annually repeated AUV surveys.Comment: MDPI Sensor

Automatic Annotation of Subsea Pipelines using Deep Learning

Regulatory requirements for sub-sea oil and gas operators mandates the frequent inspection of pipeline assets to ensure that their degradation and damage are maintained at acceptable levels. The inspection process is usually sub-contracted to surveyors who utilize sub-sea Remotely Operated Vehicles (ROVs), launched from a surface vessel and piloted over the pipeline. ROVs capture data from various sensors/instruments which are subsequently reviewed and interpreted by human operators, creating a log of event annotations; a slow, labor-intensive and costly process. The paper presents an automatic image annotation framework that identifies/classifies key events of interest in the video footage viz. exposure, burial, field joints, anodes, and free spans. The reported methodology utilizes transfer learning with a Deep Convolutional Neural Network (ResNet-50), fine-tuned on real-life, representative data from challenging sub-sea environments with low lighting conditions, sand agitation, sea-life and vegetation. The network outputs are configured to perform multi-label image classifications for critical events. The annotation performance varies between 95.1 and 99.7 in terms of accuracy and 90.4 and 99.4 in terms of F1-Score depending on event type. The performance results are on a per-frame basis and corroborate the potential of the algorithm to be the foundation for an intelligent decision support framework that automates the annotation process. The solution can execute annotations in real-time and is significantly more cost-effective than human-only approaches

A comparison of the performance of 2D and 3D convolutional neural networks for subsea survey video classification

Utilising deep learning image classification to auto-matically annotate subsea pipeline video surveys can facilitate the tedious and labour-intensive process, resulting in significant time and cost savings. However, the classification of events on subsea survey videos (frame sequences) by models trained on individual frames have been proven to vary, leading to inaccuracies. The paper extends previous work on the automatic annotation of individual subsea survey frames by comparing the performance of 2D and 3D Convolutional Neural Networks (CNNs) in classifying frame sequences. The study explores the classification of burial, exposure, free span, field joint, and anode events. Sampling and regularization techniques are designed to address the challenges of an underwater inspection video dataset owing to the environment. Results show that a 2D CNN with rolling average can outperform a 3D CNN, achieving an Exact Match Ratio of 85% and F1-Score of 90%, whilst being more computationally efficient

Plant lesion boundary delineation using lightweight deep learning with tweaking mechanism

Ever since the dawn of agriculture, the devastating consequences of plant disease inevitably impacted the crop cultivation quantitatively and qualitatively. One of the plant disease incidents happened in 2007 in Georgia which lead to a $539.74 million loss in the total revenue. Intuitively, it is essential to tackle the disease outbreaks as early as possible to diagnose the underlying cause. The detection and classification of diseases carried out by the plant pathologists are subjected to cognitive error. To alleviate direct human intervention, machine learning is undoubtedly the key to avert this downfall. Over the years, numerous neural networks have been proposed to improve the existing state-of-art. Nevertheless, minimal works have been done on segmenting the region of the disease from the leaf. On the other hand, one of the inherent issues in machine learning is “What is the optimal configuration for the network to gain the highest performance?”. Many researchers are probing, but no single solution can cater to all the models built for different purposes. The concept of fine-tuning is a critical step which generally left out of discussion due to divergence in solution. Hence, the first objective is to build a semantic segmentation network that create a salient map image tracking the boundary of the disease. The second objective is to regularize and optimize the built network to identify the optimal configuration. SegNet’s fully convolutional architecture with transfer learning is chosen as the semantic segmentation network. A total of 1000 early and late blights of potato and tomato samples from PlantVillage are fed to the model. To capture the best network, optimizers such as SGD, RMSProp and Adam are benchmarked with regularization techniques such as adaptive learning rate, dropout layer and weight & bias rates re-initialization. Afterwards, hyperparameters such as mini-batch, initial learning rate, momentum, gradient, L2 regularization, number of samples and number of epochs are tuned progressively. Throughout the tweaking process, the global accuracy and mean IoU have increased from 86.96% and 50.72% to 93.86% and 60.24% respectively. In addition, the comparison between SegNet and FCN has proven that the former architecture is lightweight and powerful in delineating the boundary of plant lesion. With the delineated lesion’s boundary, the manifestation along the leaf surface can be traced and appraised for pathological anatomy

Deep learning for internet of underwater things and ocean data analytics

The Internet of Underwater Things (IoUT) is an emerging technological ecosystem developed for connecting objects in maritime and underwater environments. IoUT technologies are empowered by an extreme number of deployed sensors and actuators. In this thesis, multiple IoUT sensory data are augmented with machine intelligence for forecasting purposes

Robust object detection in the wild via cascaded DCGAN

This research deals with the challenges of object detection at a distance or low resolution in the wild. The main intention of this research is to exploit and cascade state-of-the-art models and propose a new framework for enabling successful deployment for diverse applications. Specifically, the proposed deep learning framework uses state-of-the-art deep networks, such as Deep Convolutional Generative Adversarial Network (DCGAN) and Single Shot Detector (SSD). It combines the above two deep learning models to generate a new framework, namely DCGAN-SSD. The proposed model can deal with object detection and recognition in the wild with various image resolutions and scaling differences. To deal with multiple object detection tasks, the training of this network model in this research has been conducted using different cross-domain datasets for various applications. The efficiency of the proposed model can further be determined by the validation of diverse applications such as visual surveillance in the wild in intelligent cities, underwater object detection for crewless underwater vehicles, and on-street in-vehicle object detection for driverless vehicle technologies. The results produced by DCGAN-SSD indicate that the proposed method in this research, along with Particle Swarm Optimization (PSO), outperforms every other application concerning object detection and demonstrates its great superiority in improving object detection performance in diverse testing cases. The DCGAN-SSD model is equipped with PSO, which helps select the hyperparameter for the object detector. Most object detectors struggle in this regard, as they require manual effort in selecting the hyperparameters to obtain better object detection. This research encountered the problem of hyperparameter selection through the integration of PSO with SSD. The main reason the research conducted with deep learning models was the traditional machine learning models lag in accuracy and performance. The advantage of this research and it is achieved with the integration of DCGAN-SSD has been accommodated under a single pipeline

OIL SPILL ALONG THE TURKISH STRAITS SEA AREA; ACCIDENTS, ENVIRONMENTAL POLLUTION, SOCIO-ECONOMIC IMPACTS AND PROTECTION

The Turkish Straits Sea Area (TSSA) is a long water passage that is consisted of the Sea of Marmara, an inland sea within Turkey's borders, and two narrow straits connected to neighboring seas. With a strategic location between the Balkans and Anatolia, the Black Sea and the Mediterranean, and dominated by the continental climate, the region hosted many civilizations throughout the centuries. This makes the region among the busiest routes in the world, with sea traffic three times higher than that in the Suez Canal. The straits are the most difficult waterways to navigate and witnessed many hazardous and important collisions and accidents throughout history. In addition, this area has vital roles as a biological corridor and barrier among three distinctive marine realms. Therefore, the region is rather sensitive to damages of national and international maritime activities, which may cause severe environmental problems. This book addresses several key questions on a chapter basis, including historical accidents, background information on main dynamic restrictions, oil pollution, oil spill detection, and clean-up recoveries, its impacts on biological communities, socioeconomic aspects, and subjects with international agreements. This book will help readers, public, local and governmental authorities gain a deeper understanding of the status of the oil spill, mostly due to shipping accidents, and their related impacts along the TSSA, which needs precautionary measures to be protected.CONTENTS INTRODUCTION CHAPTER I - HISTORY OF ACCIDENTS AND REGULATIONS Remarkable Accidents at the Istanbul Strait Hasan Bora USLUER and Saim OĞUZÜLGEN …………………………………...... 3 History of Regulations before Republican Era along the Turkish Straits Sea Area Ali Umut ÜNAL …………………………………………………………………….. 16 Transition Regime in the Turkish Straits during the Republican Era Osman ARSLAN ……….……………………………………………………….……26 26 The Montreux Convention and Effects at Turkish Straits Oktay ÇETİN ………………………………………………………………….…….. 33 Evaluation of the Montreux Convention in the Light of Recent Problems Ayşenur TÜTÜNCÜ ………………………………………………………………… 44 A Historical View on Technical Developments on Ships and Effects of Turkish Straits Murat YAPICI ………………………………………………………………………. 55 CHAPTER II - GEOGRAPHY, BATHYMETRY AND HYDRO-METEOROLOGICAL CONDITIONS Geographic and Bathymetric Restrictions along the Turkish Straits Sea Area Bedri ALPAR, Hasan Bora USLUER and Şenol AYDIN ……………………..…… 61 Hydrodynamics and Modeling of Turkish Straits Serdar BEJİ and Tarkan ERDİK ………………………………………………….… 79 Wave Climate in the Turkish Sea of Marmara Tarkan ERDİK and Serdar BEJİ …………………………………………………..… 91 CHAPTER III - OIL POLLUTION, DETECTION AND RECOVERY Oil Pollution at Sea and Coast Following Major Accidents Selma ÜNLÜ ……………………………………………………………………….101 Forensic Fingerprinting in Oil-spill Source Identification at the Turkish Straits Sea Area Özlem ATEŞ DURU ……………………………………………………………… 121 xi Oil Spill Detection Using Remote Sensing Technologies-Synthetic Aperture Radar (SAR) İbrahim PAPİLA, Elif SERTEL, Şinasi KAYA and Cem GAZİOĞLU ……..……. 140 The Role of SAR Remote Sensing to Detect Oil Pollution and Emergency Intervention Saygın ABDIKAN, Çağlar BAYIK and Füsun BALIK ŞANLI ……….….……….. 157 Oil Spill Recovery and Clean-Up Techniques Emra KIZILAY, Mehtap AKBAŞ and Tahir Yavuz GEZBELİ …………………… 176 Turkish Strait Sea Area, Contingency Planning, Regulations and Case Studies Emra KIZILAY, Mehtap AKBAŞ and Tahir Yavuz GEZBELİ …………………... 188 Dispersant Response Method to Incidental Oil Pollution Dilek EDİGER, Leyla TOLUN and Fatma TELLİ KARAKOÇ ………………….... 205 CHAPTER IV - THE EFFECTS / IMPACTS OF OIL SPILL ON BIOLOGICAL COMMUNITIES – INCLUDING SAMPLING AND MONITORING Marine Microorganisms and Oil Spill Sibel ZEKİ and Pelin S. ÇİFTÇİ TÜRETKEN …………...………………………… 219 Estimated Effects of Oil Spill on the Phytoplankton Following “Volgoneft-248” Accident (Sea of Marmara) Seyfettin TAŞ ………………………………..…………………………………….... 229 Interactions between Zooplankton and Oil Spills: Lessons Learned from Global Accidents and a Proposal for Zooplankton Monitoring İ. Noyan YILMAZ and Melek İŞİNİBİLİR ……………………………………..….. 238 The Effects of Oil Spill on the Macrophytobenthic Communities Ergün TAŞKIN and Barış AKÇALI …………………………….…………….……. 244 Potential Impacts of Oil Spills on Macrozoobenthos in the Turkish Straits System Güley KURT-ŞAHİN …………………………………………………………….… 253 The Anticipated Effects of Oil Spill on Fish Populations in Case of an Accident along the Turkish Straits System – A review of Studies after Several Incidents from the World M. İdil ÖZ and Nazlı DEMİREL …………………………………………………….261 Estimated Impacts of an Oil Spill on Bird Populations along the Turkish Straits System Itri Levent ERKOL …………………………………………………………….…… 272 The Effect of Oil Spills on Cetaceans in the Turkish Straits System (TSS) Ayaka Amaha ÖZTÜRK ………………………………………………………….. 277 Changes in the Ichthyoplankton and Benthos Assemblages following Volgoneft-248 Oil Spill: Case Study Ahsen YÜKSEK and Yaprak GÜRKAN …………………………………….……. 280 Assessing the Initial and Temporal Effects of a Heavy Fuel Oil Spill on Benthic Fauna Yaprak GÜRKAN, Ahsen YÜKSEK ………………………………………..…….. 287 CHAPTER V - SOCIO-ECONOMIC ASPECTS Socio-economic Aspects of Oil Spill Özlem ATEŞ DURU and Serap İNCAZ ……………………………………….…… 301 Effects of Oil Spill on Human Health Türkan YURDUN ………………………………………………………………..…. 313 Crisis Management of Oil Spill, A Case Study: BP Gulf Mexico Oil Disaster Serap İNCAZ and Özlem ATEŞ DURU …………………………….………….……324 CHAPTER VI - CONVENTIONS RELATING TO PREVENTION OF OIL SPILL International Convention for the Prevention of Pollution of the Sea by Oil (OILPOL), 1954 and its Situation Related with Turkey Emre AKYÜZ, Metin ÇELİK and Ömer SÖNER …………………………...……... 334 International Convention for the Prevention of Pollution from Ships, 1973, as Modified by the Protocol of 1978 Relating Thereto and by the Protocol of 1997 (MARPOL) Özcan ARSLAN, Esma UFLAZ and Serap İNCAZ ………………………….……. 342 Applications of MARPOL Related with Oil Spill in Turkey Emre AKYÜZ, Özcan ASLAN and Serap İNCAZ ………………………………… 356 Ship Born Oil Pollution at the Turkish Straits Sea Area and MARPOL 73/78 Duygu ÜLKER and Sencer BALTAOĞLU………………………….…………….. 363 International Convention Relating to Intervention on the High Seas in Cases of Oil Pollution Casualties (INTERVENTION 1969) and its Applications Related with Oil Spill in Turkey Şebnem ERKEBAY ……………………………….……………………………….. 371 International Convention on Oil Pollution Preparedness, Response and Co-operation (OPRC) 1990 and its Applications Related with Oil Spill in Turkey Kadir ÇİÇEK ………………………………………………………………………. 381 Protocol on Preparedness, Response and Co-operation to Pollution Incidents by Hazardous and Noxious Substances, 2000 (OPRC-HNS Protocol) and its Effects in Turkey Aydın ŞIHMANTEPE and Cihat AŞAN ……………….…………………………. 392 The International Convention on Salvage (SALVAGE) 1989 Related with Oil Spill in Turkey İrşad BAYIRHAN ……………………………………….………………..……….. 408 CHAPTER VII - CONVENTIONS COVERING LIABILITY AND COMPENSATION RELATED WITH OIL SPILL International Convention on Civil Liability for Oil Pollution Damage (CLC), 1969 and its Applications Serap İNCAZ and Pınar ÖZDEMİR ……………………………………..………… 416 1992 Protocol to the International Convention on the Establishment of an International Fund for Compensation for Oil Pollution Damage (FUND 1992) and its Applications Related with Oil Spill in Turkey Ali Umut ÜNAL and Hasan Bora USLUER …………………………….………… 424 International Convention on Liability and Compensation for Damage in Connection with the Carriage of Hazardous and Noxious Substances by Sea (HNS), 1996 (and its 2010 Protocol) and its Applications Related with Oil Spill in Turkey Bilun ELMACIOĞLU ……………………………………………………………… 437 Bunkering Incidents and Safety Practices in Turkey Fırat BOLAT, Pelin BOLAT and Serap İNCAZ …………………………………... 447 "Nairobi International Convention on the Removal of Wrecks 2007" and its Effects on Turkey Şafak Ümit DENİZ and Serap İNCAZ ……………………….……………………. 457

Social work with airports passengers

Social work at the airport is in to offer to passengers social services. The main methodological position is that people are under stress, which characterized by a particular set of characteristics in appearance and behavior. In such circumstances passenger attracts in his actions some attention. Only person whom he trusts can help him with the documents or psychologically

Manipulador aéreo con brazos antropomórficos de articulaciones flexibles

[Resumen] Este artículo presenta el primer robot manipulador aéreo con dos brazos antropomórficos diseñado para aplicarse en tareas de inspección y mantenimiento en entornos industriales de difícil acceso para operarios humanos. El robot consiste en una plataforma aérea multirrotor equipada con dos brazos antropomórficos ultraligeros, así como el sistema de control integrado de la plataforma y los brazos. Una de las principales características del manipulador es la flexibilidad mecánica proporcionada en todas las articulaciones, lo que aumenta la seguridad en las interacciones físicas con el entorno y la protección del propio robot. Para ello se ha introducido un compacto y simple mecanismo de transmisión por muelle entre el eje del servo y el enlace de salida. La estructura en aluminio de los brazos ha sido cuidadosamente diseñada de forma que los actuadores estén aislados frente a cargas radiales y axiales que los puedan dañar. El manipulador desarrollado ha sido validado a través de experimentos en base fija y en pruebas de vuelo en exteriores.Ministerio de Economía y Competitividad; DPI2014-5983-C2-1-