MARVEL: A Large-Scale Image Dataset for Maritime Vessels

Gundogdu, Erhan; Solmaz, Berkan; Yücesoy, Veysel; Koç, Aykut

doi:10.1007/978-3-319-54193-8_11

Cited by 58 publications

(45 citation statements)

References 26 publications

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“…foreach frame at current time t detect all potential vessels V = {v k } N k=1 from the F t frame using M d53 foreach tracklet of vessel // Association gate generated from prior time Predict the kinematic track gate from MM filter from time t − 1 & adaptive association gate of appearance endfor MC data association using simulated annealing algorithm foreach pair of matched detection and tracklet tracklet update by MM filter endfor foreach detection vessel v k not associated with any tracklets in T We performed extensive experiments on SMD [1] and the PETS 2016 maritime dataset [6] to evaluate performance of the proposed M 3 C. The detector was pretrained on a Marvel vessel image dataset by the offline method [44].…”

Section: Algorithm 1: the Proposed M 3 C Tracking By Detectionmentioning

confidence: 99%

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

et al. 2019

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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.

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Section: Algorithm 1: the Proposed M 3 C Tracking By Detectionmentioning

confidence: 99%

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

et al. 2019

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“…We used YOLOv3 [36] to automatically locate the bounding box (BBox) of a vessel by using the pretrained weight from ImageNet and then fine-tuning it on the MARVEL dataset [37]. Table 1 displays the feature map and anchor box of the VesselID-539 dataset.…”

Section: B Annotation Descriptionmentioning

confidence: 99%

Marine Vessel Re-Identification: A Large-Scale Dataset and Global-and-Local Fusion-Based Discriminative Feature Learning

et al. 2020

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A marine vessel re-identification system has to determine whether or not different images represent the same vessel. Accurate vessel re-identification improves onshore closed-circuit television monitoring in a vessel traffic services system as well as onboard surveillance of surrounding vessels. However, because ships are rigid bodies and the marine environment is harsh, the accurate re-identification of vessels at sea can be very difficult. We describe a marine vessel-re-identification framework, Globaland-Local Fusion-based Multi-view Feature Learning (GLF-MVFL), which is based on a combination of global and fine-grained local features. GLF-MVFL combines cross-entropy loss with our newly-developed orientation-guided quintuplet loss. We exploit intrinsic features of marine vessels to optimize multi-view representation learning for re-identification. GLF-MVFL uses ResNet-50 as the backbone network to extract features for simultaneous quintuple input. It detects and discriminates between features and estimates viewpoints to form a comprehensive re-identification framework. We created an annotated large-scale vessel retrieval dataset, VesselID-539, which contains images from viewpoints similar to those of an autonomous surface vessel, to use in evaluating the performance of the model. Extensive experiments and analysis of the results obtained from using VesselID-539 demonstrate that our approach significantly increases the accuracy of vessel re-identification and is more effective and robust for images from different viewpoints than other approaches. INDEX TERMS Autonomous surface vessel (ASV), maritime surveillance, VesselID-539 dataset, multi views, vessel re-identification (V-ReID).

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“…Besides, rare categories are hard to collect, and a few instances toughly train a deep CNNs of a satisfying accuracy. The visible and infrared ship imagery (VAIS) coarse-grained dataset [13] and the MARitime VEsseLs (MARVEL) finegrained dataset [14] are the few publicly available datasets in the maritime image classification. However, the class imbalance appears in the datasets, which affect the result of image classification [6], [15].…”

Section: Introductionmentioning

confidence: 99%

Maritime Visible Image Classification Based on Double Transfer Method

et al. 2020

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Image classification using deep transfer learning has received significant attention, benefiting from pre-trained with the large-scale annotation dataset and continuous improvement of neural network structure. In contrast to universal image classification, however, few publicly available datasets of maritime environments utilize deep transfer learning. Due to data-gathering effort and computational cost, the maritime datasets are deficient in the method of merging datasets and the benchmark of few-shot dataset classifiers. In this paper, we proposed the double transfer method, consisting of the merging datasets network and the backbone network, to address the problem. The merging datasets network measuring image similarity separates classes of known and unknown samples to reorganize a dataset, and the backbone network is constructed from the model EfficientNet-b5 by network-based deep transfer learning. Using the merging datasets network, we introduce the visible maritime image dataset, which has 3,750 images and twenty-five classes, including multitudinous maritime objects. The backbone networks evaluated and analyzed the dataset based on accuracy, precision, recall, and F-measure metrics. Using the double transfer method, we can achieve an accuracy of 91.39% in the visible maritime image dataset.

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MARVEL: A Large-Scale Image Dataset for Maritime Vessels

Cited by 58 publications

References 26 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

Marine Vessel Re-Identification: A Large-Scale Dataset and Global-and-Local Fusion-Based Discriminative Feature Learning

Maritime Visible Image Classification Based on Double Transfer Method

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