Deep Learning-Based Ship Detection in Remote Sensing Imagery Using TensorFlow

Apoorva, A; Mishra, Gopal Krishna; Sahoo, Rashmita; Bhoi, Sourav Kumar; Mallick, Chittaranjan

doi:10.1007/978-981-15-5243-4_14

Cited by 5 publications

(3 citation statements)

References 14 publications

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“…In addition, a more efficient deep learning framework suitable for big data processing has become one of the current research hotspots. In recent years, many wellknown distributed deep learning computing frameworks have been proposed, such as Google's DistBelief [34], Baidu's DeepImage [35], SparkNet [36], TensorFlowOnSpark [37] and BigDL [38], etc. 2) BigDL: Dai et al [38] point out that the current mainstream distributed deep learning frameworks (CaffeOn-Spark [39], TensorFlowOnSpark [37], etc.)…”

Section: B Distributed Deep Learning Computing Frameworkmentioning

confidence: 99%

“…In recent years, many wellknown distributed deep learning computing frameworks have been proposed, such as Google's DistBelief [34], Baidu's DeepImage [35], SparkNet [36], TensorFlowOnSpark [37] and BigDL [38], etc. 2) BigDL: Dai et al [38] point out that the current mainstream distributed deep learning frameworks (CaffeOn-Spark [39], TensorFlowOnSpark [37], etc.) all adopt a "connector approach" method and use an integrated workflow to develop suitable interfaces to connect different data processing and deep learning components.…”

Section: B Distributed Deep Learning Computing Frameworkmentioning

confidence: 99%

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Object Detection in Large-Scale Remote Sensing Images With a Distributed Deep Learning Framework

Liu

Yan

et al. 2022

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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With the accumulation and storage of remote sensing images in various satellite data centers, the rapid detection of objects of interest from large-scale remote sensing images is a current research focus and application requirement. Although some cutting-edge object detection algorithms in remote sensing images perform well in terms of accuracy (mAP), their inference speed is slow and requires high hardware requirements that are not suitable for real-time object detection in large-scale remote sensing images. To address this issue, we propose a fast inference framework for object detection in large-scale remote sensing images. On the one hand, we introduce α-IoU Loss on the YWCSL model to implement adaptive weighted loss and gradient, which achieves 64.62% and 79.54% mAP on DIOR-R and DOTA test sets, respectively. More importantly, the inference speed of the YWCSL model reaches 60.74 FPS on a single NVIDIA GeForce RTX 3080 Ti, which is 2.87 times faster than the current state-of-the-art one-stage detector S 2 A-Net. On the other hand, we build a distributed inference framework to enable fast inference on large-scale remote sensing images. Specifically, we save the images on HDFS for distributed storage and deploy the pretrained YWCSL model on the Spark cluster. In addition, we use a custom partitioner RankPartition to repartition the data to further improve the performance of the cluster. When using 5 nodes, the speedup of the cluster reaches 9.54, which is 90.80% higher than the theoretical linear speedup (5.00). Our distributed inference framework for large-scale remote sensing images significantly reduces the dependence of object detection on expensive hardware resources, which has important research significance for the wide application of object detection in remote sensing images.

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Section: B Distributed Deep Learning Computing Frameworkmentioning

confidence: 99%

Section: B Distributed Deep Learning Computing Frameworkmentioning

confidence: 99%

Object Detection in Large-Scale Remote Sensing Images With a Distributed Deep Learning Framework

Liu

Yan

et al. 2022

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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“…The tweets are classi ed into 3 polarity; positive, negative, neutral sentiment. Our model consists of results from 3 types of Machine Learning models [17][18][19][20][21][22][23][24][25][26][27][28][29][30][31]; Decision Tree, SVM, and Logistic Regression. The simulation is performed better under this environment with 88.67% accuracy in SVM.…”

Section: Trainingmentioning

confidence: 99%

A Social Media Sentiment Analysis: Machine Intelligence Model For Worldwide Covid-19 Vaccination Using Twitter Data

Jena

Bhoi

Behera

et al. 2021

Preprint

Self Cite

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Understanding human emotions is one of the crucial aspects when we are to take action. Our emotions dictate our apparent behaviors. In simple words, what we feel inside can predict things about what we would do. This creates a huge opportunity for government and businesses industry to understand and predict people's behaviors. There has been some really great research done on this with high accuracy. Recently, Covid-19 vaccination process is a challenging task going on all over the world and it is necessary to explore people’s reaction over this for more effective vaccination process spread. In this paper, wetried to understand an event (Covid-19 vaccination) with a relatively simple model with decent accuracy compared to other sophisticated models. We use simple machine learning models to train and deploy it over the network. We have used KNIME Analytical Platform to design and implement our model as it provides end-to-end analytics. We have managed to get 88.67% accuracy and Cohen’s kappa 0.789 with SVM model by tuning some parameters. The model is deployed on Twitter data. This paper shows our efforts trying to make a simple model to analyze an event (Covid-19 vaccination) and understand people's emotions towards the event. The methodology involves identifying important topics (terms) and finding out the sentiment (positive, negative, neutral). This paper tries to find a low-cost solution to analyze an event and provide data-driven insights from it without involving sophisticated algorithms.

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