Journal ArticleDOI
An enhanced CNN-enabled learning method for promoting ship detection in maritime surveillance system
TLDR
An enhanced convolutional neural network (CNN) is proposed to improve ship detection under different weather conditions by redesigning the sizes of anchor boxes, predicting the localization uncertainties of bounding boxes, introducing the soft non-maximum suppression, and reconstructing a mixed loss function.About:
This article is published in Ocean Engineering.The article was published on 2021-09-01. It has received 118 citations till now. The article focuses on the topics: Robustness (computer science).read more
Citations
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Journal ArticleDOI
Deep Learning Model for the Automatic Classification of White Blood Cells
TL;DR: A deep learning (D.L) model is implemented that uses the DenseNet121 model to classify the different types of white blood cells (WBC) and has outperformed with batch size 8 as compared to other batch sizes.
Journal ArticleDOI
A machine learning method for the evaluation of ship grounding risk in real operational conditions
TL;DR: In this paper , a machine learning method was used to evaluate ship grounding risk in real environmental conditions using big data streams from Automatic Identification System (AIS), nowcast data, and seafloor depth data from the General Bathymetric Chart of the Oceans (GEBCO).
Journal ArticleDOI
Lightweight deep network-enabled real-time low-visibility enhancement for promoting vessel detection in maritime video surveillance
Yu Guo,Yuxu Lu,Ryan Wen Liu +2 more
Journal ArticleDOI
A Variational Framework for Underwater Image Dehazing and Deblurring
TL;DR: Guojia et al. as discussed by the authors proposed a red channel prior guided variational framework based on the complete underwater image formation model (UIFM) for underwater image deblurring.
Journal ArticleDOI
Deep Network-Enabled Haze Visibility Enhancement for Visual IoT-Driven Intelligent Transportation Systems
TL;DR: TSDNet as discussed by the authors proposes a deep network-enabled three-stage dehazing network (termed TSDNet) for promoting the visual IoT-driven intelligent transportation systems (ITSs).
References
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Proceedings ArticleDOI
You Only Look Once: Unified, Real-Time Object Detection
TL;DR: Compared to state-of-the-art detection systems, YOLO makes more localization errors but is less likely to predict false positives on background, and outperforms other detection methods, including DPM and R-CNN, when generalizing from natural images to other domains like artwork.
Journal ArticleDOI
Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
TL;DR: This work introduces a Region Proposal Network (RPN) that shares full-image convolutional features with the detection network, thus enabling nearly cost-free region proposals and further merge RPN and Fast R-CNN into a single network by sharing their convolutionAL features.
Posted Content
Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
TL;DR: Faster R-CNN as discussed by the authors proposes a Region Proposal Network (RPN) to generate high-quality region proposals, which are used by Fast R-NN for detection.
Proceedings ArticleDOI
Rich Feature Hierarchies for Accurate Object Detection and Semantic Segmentation
TL;DR: RCNN as discussed by the authors combines CNNs with bottom-up region proposals to localize and segment objects, and when labeled training data is scarce, supervised pre-training for an auxiliary task, followed by domain-specific fine-tuning, yields a significant performance boost.
Book ChapterDOI
SSD: Single Shot MultiBox Detector
Wei Liu,Dragomir Anguelov,Dumitru Erhan,Christian Szegedy,Scott Reed,Cheng-Yang Fu,Alexander C. Berg +6 more
TL;DR: The approach, named SSD, discretizes the output space of bounding boxes into a set of default boxes over different aspect ratios and scales per feature map location, which makes SSD easy to train and straightforward to integrate into systems that require a detection component.