A Multi-Task Deep Learning Architecture for Maritime Surveillance Using AIS Data Streams

Nguyen, Duong; Vadaine, Rodolphe; Hajduch, Guillaume; Garello, R.; Fablet, Ronan

doi:10.1109/dsaa.2018.00044

Cited by 90 publications

(78 citation statements)

References 21 publications

(35 reference statements)

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…We used GRU with 128 units for the vessel's maneuverability discrimination and trained the model while using Adam with a dropout of 0.2 for regularization and with a learning rate of 0.001. Due to the lack of adequate vessel tracklets or video galleries for training, it was pretrained by the public AIS dataset [45], and was then fine-tuned on the training set partitioned from SMD and PETS 2016.…”

Section: Implementation Detailsmentioning

confidence: 99%

M3C: Multimodel-and-Multicue-Based Tracking by Detection of Surrounding Vessels in Maritime Environment for USV

et al. 2019

View full text Add to dashboard Cite

It is crucial for unmanned surface vessels (USVs) to detect and track surrounding vessels in real time to avoid collisions at sea. However, the harsh maritime environment poses great challenges to multitarget tracking (MTT). In this paper, a novel tracking by detection framework that integrates the multimodel and multicue (M3C) pipeline is proposed, which aims at improving the detection and tracking performance. Regarding the multimodel, we predicted the maneuver probability of a target vessel via the gated recurrent unit (GRU) model with an attention mechanism, and fused their respective outputs as the output of a kinematic filter. We developed a hybrid affinity model based on multi cues, such as the motion, appearance, and attitude of the ego vessel in the data association stage. By using the proposed ship re-identification approach, the tracker had the capability of appearance matching via metric learning. Experimental evaluation of two public maritime datasets showed that our method achieved state-of-the-art performance, not only in identity switches (IDS) but also in frame rates.

show abstract

Section: Implementation Detailsmentioning

confidence: 99%

M3C: Multimodel-and-Multicue-Based Tracking by Detection of Surrounding Vessels in Maritime Environment for USV

et al. 2019

View full text Add to dashboard Cite

show abstract

“…On the other hand, unlike the vessels in the sea, the mobilities of cars or people on land are mostly restricted to streets or rails, therefore, the traffic pattern for which is comparatively easy to be revealed. Moreover, we would like to note that, a variety of research with respects to the deep learning in maritime have been conducted, however most of them focus on vessel type identification [27], trajectory prediction or reconstruction [28], anomaly detection [29,30] and collision avoidance [31][32][33]. The problem of how to adopt deep learning to forecast the traffic flows for maritime has not yet been studied.…”

Section: Introductionmentioning

confidence: 99%

Using Deep Learning to Forecast Maritime Vessel Flows

Zhou

Liu

Wang

et al. 2020

Sensors

View full text Add to dashboard Cite

Forecasting vessel flows is important to the development of intelligent transportation systems in the maritime field, as real-time and accurate traffic information has favorable potential in helping a maritime authority to alleviate congestion, mitigate emission of GHG (greenhouse gases) and enhance public safety, as well as assisting individual vessel users to plan better routes and reduce additional costs due to delays. In this paper, we propose three deep learning-based solutions to forecast the inflow and outflow of vessels within a given region, including a convolutional neural network (CNN), a long short-term memory (LSTM) network, and the integration of a bidirectional LSTM network with a CNN (BDLSTM-CNN). To apply those solutions, we first divide the given maritime region into M × N grids, then we forecast the inflow and outflow for all the grids. Experimental results based on the real AIS (Automatic Identification System) data of marine vessels in Singapore demonstrate that the three deep learning-based solutions significantly outperform the conventional method in terms of mean absolute error and root mean square error, with the performance of the BDLSTM-CNN-based hybrid solution being the best.

show abstract

“…Though not authorized, ships can easily turn off their AIS and/or spoof their identity. While AIS tracking strategies [9] may be considered to address missing track segments, the evaluation of spoofing behavior is a complex task. Iphar et al [10] evaluated that amongst ships with AIS, about 6% have no specified type, and 3% are only described as "vessels".…”

Section: Introductionmentioning

confidence: 99%

Ship Identification and Characterization in Sentinel-1 SAR Images with Multi-Task Deep Learning

et al. 2019

Self Cite

View full text Add to dashboard Cite

The monitoring and surveillance of maritime activities are critical issues in both military and civilian fields, including among others fisheries’ monitoring, maritime traffic surveillance, coastal and at-sea safety operations, and tactical situations. In operational contexts, ship detection and identification is traditionally performed by a human observer who identifies all kinds of ships from a visual analysis of remotely sensed images. Such a task is very time consuming and cannot be conducted at a very large scale, while Sentinel-1 SAR data now provide a regular and worldwide coverage. Meanwhile, with the emergence of GPUs, deep learning methods are now established as state-of-the-art solutions for computer vision, replacing human intervention in many contexts. They have been shown to be adapted for ship detection, most often with very high resolution SAR or optical imagery. In this paper, we go one step further and investigate a deep neural network for the joint classification and characterization of ships from SAR Sentinel-1 data. We benefit from the synergies between AIS (Automatic Identification System) and Sentinel-1 data to build significant training datasets. We design a multi-task neural network architecture composed of one joint convolutional network connected to three task specific networks, namely for ship detection, classification, and length estimation. The experimental assessment shows that our network provides promising results, with accurate classification and length performance (classification overall accuracy: 97.25%, mean length error: 4.65 m ± 8.55 m).

show abstract

A Multi-Task Deep Learning Architecture for Maritime Surveillance Using AIS Data Streams

Cited by 90 publications

References 21 publications

M3C: Multimodel-and-Multicue-Based Tracking by Detection of Surrounding Vessels in Maritime Environment for USV

M3C: Multimodel-and-Multicue-Based Tracking by Detection of Surrounding Vessels in Maritime Environment for USV

Using Deep Learning to Forecast Maritime Vessel Flows

Ship Identification and Characterization in Sentinel-1 SAR Images with Multi-Task Deep Learning

Contact Info

Product

Resources

About