Maritime Ship Targets Recognition with Deep Learning

Fu, Huixuan; Li, Yuan; Wang, Yuchao; Li, Peng

doi:10.23919/chicc.2018.8484085

Cited by 15 publications

(10 citation statements)

References 3 publications

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“…For the acquisition of visual information on ships in the sea, there is a limitation in that drones are subject to the Beyond Visual Line of Sight (BVLOS). Therefore, most studies on the visual interpretation of ships focus on public datasets (MODD, SMD, IPATCH, SAGULL, and VOC2007) [ 9 , 16 ] and image searches on Google [ 7 , 8 ]. It is also known that there are no studies with datasets obtained through the direct imaging of drones.…”

Section: Dataset Of Marine Traffic Management Net (Mtmnet)mentioning

confidence: 99%

Application of Convolutional Neural Network (CNN) to Recognize Ship Structures

Lim

Kim

Hong

et al. 2022

Sensors

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The purpose of this paper is to study the recognition of ships and their structures to improve the safety of drone operations engaged in shore-to-ship drone delivery service. This study has developed a system that can distinguish between ships and their structures by using a convolutional neural network (CNN). First, the dataset of the Marine Traffic Management Net is described and CNN’s object sensing based on the Detectron2 platform is discussed. There will also be a description of the experiment and performance. In addition, this study has been conducted based on actual drone delivery operations—the first air delivery service by drones in Korea.

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Section: Dataset Of Marine Traffic Management Net (Mtmnet)mentioning

confidence: 99%

Application of Convolutional Neural Network (CNN) to Recognize Ship Structures

Lim

Kim

Hong

et al. 2022

Sensors

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“…With the development of artificial intelligence technology, many intelligent detection technologies are applied to VTS [4,5] and smart ships [6,7]. Among them, the applications of deep learning technology in the detection and classification of ships are currently widely used [8,9]. The purpose of this type of application is to supplement information about ships not in AIS [10].…”

Section: Introductionmentioning

confidence: 99%

Intelligent Recognition System Based on Contour Accentuation for Navigation Marks

Sun

Qiu

et al. 2021

Wireless Communications and Mobile Computing

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Sensing navigational environment represented by navigation marks is an important task for unmanned ships and intelligent navigation systems, and the sensing can be performed by recognizing the images from a camera. In order to improve the image recognition accuracy, this paper combined a contour accentuation algorithm into a multiple scale attention mechanism-based classification model for navigation marks. Experimental results show that the method increases the accuracy of navigation mark classification from 95.98% to 96.53%. Based on the classification model, an intelligent navigation mark recognition system was developed for the Changjiang Nanjing Waterway Bureau, in which the model is deployed and updated by the TensorFlow Serving.

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“…For intelligent sensing of navigational environment, many studies about ship classification and detection based on deep learning techniques have been carried out gradually [ 7 , 8 ]; however, few research focused on the recognition of navigational features so far. In 2019, we started a research on the recognition of navigation marks during daytime and proposed a classification model based on ResNet-50 and a multiple scale attention mechanism [ 9 ].…”

Section: Introductionmentioning

confidence: 99%

Multilabel Video Classification Model of Navigation Mark’s Lights Based on Deep Learning

Han

Pan

et al. 2021

Computational Intelligence and Neuroscience

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At night, buoys and other navigation marks disappear to be replaced by fixed or flashing lights. Navigation marks are seen as a set of lights in various colors rather than their familiar outline. Deciphering that the meaning of the lights is a burden to navigators, it is also a new challenging research direction of intelligent sensing of navigation environment. The study studied initiatively the intelligent recognition of lights on navigation marks at night based on multilabel video classification methods. To capture effectively the characteristics of navigation mark’s lights, including both color and flashing phase, three different multilabel classification models based on binary relevance, label power set, and adapted algorithm were investigated and compared. According to the experiment’s results performed on a data set with 8000 minutes video, the model based on binary relevance, named NMLNet, has highest accuracy about 99.23% to classify 9 types of navigation mark’s lights. It also has the fastest computation speed with least network parameters. In the NMLNet, there are two branches for the classifications of color and flashing, respectively, and for the flashing classification, an improved MobileNet-v2 was used to capture the brightness characteristic of lights in each video frame, and an LSTM is used to capture the temporal dynamics of lights. Aiming to run on mobile devices on vessel, the MobileNet-v2 was used as backbone, and with the improvement of spatial attention mechanism, it achieved the accuracy near Resnet-50 while keeping its high speed.

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Maritime Ship Targets Recognition with Deep Learning

Cited by 15 publications

References 3 publications

Application of Convolutional Neural Network (CNN) to Recognize Ship Structures

Application of Convolutional Neural Network (CNN) to Recognize Ship Structures

Intelligent Recognition System Based on Contour Accentuation for Navigation Marks

Multilabel Video Classification Model of Navigation Mark’s Lights Based on Deep Learning

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