scholarly journals AUTOMATED MARINE OIL SPILL DETECTION USING DEEP LEARNING INSTANCE SEGMENTATION MODEL

2020 ◽  
Vol XLIII-B3-2020 ◽  
pp. 1271-1276
Author(s):  
S. T. Yekeen ◽  
A.-L. Balogun
Keyword(s):  
Deep Learning ◽  
Oil Spill ◽  
Semantic Segmentation ◽  
Learning Models ◽  
Marine Oil ◽  
Conventional Machine ◽  
Feature Pyramid ◽  
Model Training ◽  
Instance Segmentation ◽  
Better Than

Abstract. This study developed a novel deep learning oil spill instance segmentation model using Mask-Region-based Convolutional Neural Network (Mask R-CNN) model which is a state-of-the-art computer vision model. A total of 2882 imageries containing oil spill, look-alike, ship, and land area after conducting different pre-processing activities were acquired. These images were subsequently sub-divided into 88% training and 12% for testing, equating to 2530 and 352 images respectively. The model training was conducted using transfer learning on a pre-trained ResNet 101 with COCO data as a backbone in combination with Feature Pyramid Network (FPN) architecture for the extraction of features at 30 epochs with 0.001 learning rate. The model’s performance was evaluated using precision, recall, and F1-measure which shows a higher performance than other existing models with value of 0.964, 0.969 and 0.968 respectively. As a specialized task, the study concluded that the developed deep learning instance segmentation model (Mask R-CNN) performs better than conventional machine learning models and semantic segmentation deep learning models in detection and segmentation of marine oil spill.

Introducing AIDE: a Software Suite for Annotating Images with Deep and Active Learning Assistance

2021 ◽  
Author(s):  
Benjamin Kellenberger ◽  
Devis Tuia ◽  
Dan Morris
Keyword(s):  
Deep Learning ◽  
Active Learning ◽  
Expert Knowledge ◽  
Semantic Segmentation ◽  
Third Party ◽  
Learning Models ◽  
Web Browser ◽  
Web Based ◽  
Model Training ◽  
Bounding Boxes

<p>Ecological research like wildlife censuses increasingly relies on data on the scale of Terabytes. For example, modern camera trap datasets contain millions of images that require prohibitive amounts of manual labour to be annotated with species, bounding boxes, and the like. Machine learning, especially deep learning [3], could greatly accelerate this task through automated predictions, but involves expansive coding and expert knowledge.</p><p>In this abstract we present AIDE, the Annotation Interface for Data-driven Ecology [2]. In a first instance, AIDE is a web-based annotation suite for image labelling with support for concurrent access and scalability, up to the cloud. In a second instance, it tightly integrates deep learning models into the annotation process through active learning [7], where models learn from user-provided labels and in turn select the most relevant images for review from the large pool of unlabelled ones (Fig. 1). The result is a system where users only need to label what is required, which saves time and decreases errors due to fatigue.</p><p><img src="https://contentmanager.copernicus.org/fileStorageProxy.php?f=gnp.0402be60f60062057601161/sdaolpUECMynit/12UGE&app=m&a=0&c=131251398e575ac9974634bd0861fadc&ct=x&pn=gnp.elif&d=1" alt=""></p><p><em>Fig. 1: AIDE offers concurrent web image labelling support and uses annotations and deep learning models in an active learning loop.</em></p><p>AIDE includes a comprehensive set of built-in models, such as ResNet [1] for image classification, Faster R-CNN [5] and RetinaNet [4] for object detection, and U-Net [6] for semantic segmentation. All models can be customised and used without having to write a single line of code. Furthermore, AIDE accepts any third-party model with minimal implementation requirements. To complete the package, AIDE offers both user annotation and model prediction evaluation, access control, customisable model training, and more, all through the web browser.</p><p>AIDE is fully open source and available under https://github.com/microsoft/aerial_wildlife_detection.</p><p> </p><p><strong>References</strong></p>

A novel deep learning instance segmentation model for automated marine oil spill detection

2020 ◽  
Vol 167 ◽  
pp. 190-200 ◽  
Author(s):  
Shamsudeen Temitope Yekeen ◽  
Abdul‐Lateef Balogun ◽  
Khamaruzaman B. Wan Yusof
Keyword(s):  
Deep Learning ◽  
Oil Spill ◽  
Marine Oil ◽  
Oil Spill Detection ◽  
Instance Segmentation

Recent Advances in Predicting ncRNA-Protein Interactions Based on Machine Learning

2021 ◽  
Vol 01 ◽  
Author(s):  
Jingjing Wang ◽  
Yanpeng Zhao ◽  
Xiaoqian Huang ◽  
Yi Shi ◽  
Jianjun Tan
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Protein Interactions ◽  
Predictive Performance ◽  
Research Approach ◽  
Learning Models ◽  
Recent Advances ◽  
Conventional Machine ◽  
Near Future ◽  
Better Than

: Non-coding RNAs (ncRNAs) play significant roles in various physiological and pathological processes via interacting with the proteins. The existing experimental methods used for predicting ncRNA-protein interactions are costly and time-consuming. Therefore, an increasing number of machine learning models have been developed to efficiently predict ncRNA-protein interactions (ncRPIs), including shallow machine learning and deep learning models, which have achieved dramatic achievement on the identification of ncRPIs. In this review, we provided an overview of the recent advances in various machine learning methods for predicting ncRPIs, mainly focusing on ncRNAs-protein interaction databases, classical datasets, ncRNA/protein sequence encoding methods, conventional machine learning-based models, deep learning-based models, and the two integration-based models. Furthermore, we compared the reported accuracy of these approaches and discussed the potential and limitations of deep learning applications in ncRPIs. It was found that the predictive performance of integrated deep learning is the best, and those deep learning-based methods do not always perform better than shallow machine learning-based methods. We discussed the potential of using deep learning and proposed a research approach on the basis of the existing research. We believe that the model based on integrated deep learning is able to achieve higher accuracy in the prediction if substantial experimental data were available in the near future.

scholarly journals InstantDL: an easy-to-use deep learning pipeline for image segmentation and classification

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Dominik Jens Elias Waibel ◽  
Sayedali Shetab Boushehri ◽  
Carsten Marr
Keyword(s):  
Image Processing ◽  
Deep Learning ◽  
Specific Problem ◽  
State Of The Art ◽  
Image Data ◽  
Semantic Segmentation ◽  
Parameter Tuning ◽  
Cellular Processes ◽  
Minimal Effort ◽  
Instance Segmentation

Abstract Background Deep learning contributes to uncovering molecular and cellular processes with highly performant algorithms. Convolutional neural networks have become the state-of-the-art tool to provide accurate and fast image data processing. However, published algorithms mostly solve only one specific problem and they typically require a considerable coding effort and machine learning background for their application. Results We have thus developed InstantDL, a deep learning pipeline for four common image processing tasks: semantic segmentation, instance segmentation, pixel-wise regression and classification. InstantDL enables researchers with a basic computational background to apply debugged and benchmarked state-of-the-art deep learning algorithms to their own data with minimal effort. To make the pipeline robust, we have automated and standardized workflows and extensively tested it in different scenarios. Moreover, it allows assessing the uncertainty of predictions. We have benchmarked InstantDL on seven publicly available datasets achieving competitive performance without any parameter tuning. For customization of the pipeline to specific tasks, all code is easily accessible and well documented. Conclusions With InstantDL, we hope to empower biomedical researchers to conduct reproducible image processing with a convenient and easy-to-use pipeline.

scholarly journals Self-Attention in Reconstruction Bias U-Net for Semantic Segmentation of Building Rooftops in Optical Remote Sensing Images

2021 ◽  
Vol 13 (13) ◽  
pp. 2524
Author(s):  
Ziyi Chen ◽  
Dilong Li ◽  
Wentao Fan ◽  
Haiyan Guan ◽  
Cheng Wang ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Deep Learning ◽  
Semantic Segmentation ◽  
Extraction Methods ◽  
The Self ◽  
Optical Remote Sensing ◽  
Building Extraction ◽  
Learning Models ◽  
Remote Sensing Images ◽  
Segmentation Methods

Deep learning models have brought great breakthroughs in building extraction from high-resolution optical remote-sensing images. Among recent research, the self-attention module has called up a storm in many fields, including building extraction. However, most current deep learning models loading with the self-attention module still lose sight of the reconstruction bias’s effectiveness. Through tipping the balance between the abilities of encoding and decoding, i.e., making the decoding network be much more complex than the encoding network, the semantic segmentation ability will be reinforced. To remedy the research weakness in combing self-attention and reconstruction-bias modules for building extraction, this paper presents a U-Net architecture that combines self-attention and reconstruction-bias modules. In the encoding part, a self-attention module is added to learn the attention weights of the inputs. Through the self-attention module, the network will pay more attention to positions where there may be salient regions. In the decoding part, multiple large convolutional up-sampling operations are used for increasing the reconstruction ability. We test our model on two open available datasets: the WHU and Massachusetts Building datasets. We achieve IoU scores of 89.39% and 73.49% for the WHU and Massachusetts Building datasets, respectively. Compared with several recently famous semantic segmentation methods and representative building extraction methods, our method’s results are satisfactory.

scholarly journals EFFECT OF INQUIRY TRAINING LEARNING MODEL DAN CREATIVITY ON SCIENCE PROCESS SKILLS

2016 ◽  
Vol 5 (1) ◽  
pp. 44 ◽  
Author(s):  
Purnama Silitonga ◽  
Mara Bangun Harahap ◽  
Derlina .
Keyword(s):  
Interaction Model ◽  
Sampling Technique ◽  
Learning Model ◽  
Process Skills ◽  
Learning Models ◽  
Science Process Skills ◽  
The Difference ◽  
Model Training ◽  
Cluster Random Sampling ◽  
Better Than

This study aims: 1) to determine differences in science process skills of students with learning model inquiry training and conventional learning models, 2) to determine the difference science process skills of students who have high creativity and creativity is low, 3) to determine the interaction model of learning inquiry trainingwith creativity of the science process skills. The sampling technique conducted cluster random sampling two classes, where first class as a class experiment with the number of students 32 people applied learning model inquiry training (X-1) and the second class as a class control the number of students 32 people who applied conventional learning model ( X-2). Instruments in this study is the science process skills test and a test of creativity in the form of a description. From these results it can be concluded that: 1) science process skills of students that learned with a learning model inquiry training is better than the students that learned with conventional learning models, 2)science process skills of students with high creativity better than students with creativity is low, 3) there is interaction between inquirylearning model training and creativity in influencing the science process skills of students.

scholarly journals Semantic Segmentation of Urban Buildings Using a High-Resolution Network (HRNet) with Channel and Spatial Attention Gates

2021 ◽  
Vol 13 (16) ◽  
pp. 3087
Author(s):  
Seonkyeong Seong ◽  
Jaewan Choi
Keyword(s):  
Deep Learning ◽  
High Resolution ◽  
Spatial Attention ◽  
Semantic Segmentation ◽  
Aerial Images ◽  
Building Extraction ◽  
Learning Models ◽  
Urban Buildings

In this study, building extraction in aerial images was performed using csAG-HRNet by applying HRNet-v2 in combination with channel and spatial attention gates. HRNet-v2 consists of transition and fusion processes based on subnetworks according to various resolutions. The channel and spatial attention gates were applied in the network to efficiently learn important features. A channel attention gate assigns weights in accordance with the importance of each channel, and a spatial attention gate assigns weights in accordance with the importance of each pixel position for the entire channel. In csAG-HRNet, csAG modules consisting of a channel attention gate and a spatial attention gate were applied to each subnetwork of stage and fusion modules in the HRNet-v2 network. In experiments using two datasets, it was confirmed that csAG-HRNet could minimize false detections based on the shapes of large buildings and small nonbuilding objects compared to existing deep learning models.

scholarly journals Transformer-Based Decoder Designs for Semantic Segmentation on Remotely Sensed Images

2021 ◽  
Vol 13 (24) ◽  
pp. 5100
Author(s):  
Teerapong Panboonyuen ◽  
Kulsawasd Jitkajornwanich ◽  
Siam Lawawirojwong ◽  
Panu Srestasathiern ◽  
Peerapon Vateekul
Keyword(s):  
Image Processing ◽  
Deep Learning ◽  
Natural Language Processing ◽  
Language Processing ◽  
State Of The Art ◽  
Semantic Segmentation ◽  
Landsat 8 ◽  
Convolutional Network ◽  
Image Labeling ◽  
Feature Pyramid

Transformers have demonstrated remarkable accomplishments in several natural language processing (NLP) tasks as well as image processing tasks. Herein, we present a deep-learning (DL) model that is capable of improving the semantic segmentation network in two ways. First, utilizing the pre-training Swin Transformer (SwinTF) under Vision Transformer (ViT) as a backbone, the model weights downstream tasks by joining task layers upon the pretrained encoder. Secondly, decoder designs are applied to our DL network with three decoder designs, U-Net, pyramid scene parsing (PSP) network, and feature pyramid network (FPN), to perform pixel-level segmentation. The results are compared with other image labeling state of the art (SOTA) methods, such as global convolutional network (GCN) and ViT. Extensive experiments show that our Swin Transformer (SwinTF) with decoder designs reached a new state of the art on the Thailand Isan Landsat-8 corpus (89.8% F1 score), Thailand North Landsat-8 corpus (63.12% F1 score), and competitive results on ISPRS Vaihingen. Moreover, both our best-proposed methods (SwinTF-PSP and SwinTF-FPN) even outperformed SwinTF with supervised pre-training ViT on the ImageNet-1K in the Thailand, Landsat-8, and ISPRS Vaihingen corpora.

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