Generic and attribute-specific deep representations for maritime vessels

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

doi:10.1186/s41074-017-0033-4

Cited by 12 publications

(8 citation statements)

References 33 publications

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“…(iv) Net-C1-C2-Quad is the network trained according to the loss in (8), taking also the quadruplet pairs into consideration. (v) Net-C1-C2-Quad-G is the network trained according to the loss in (17), imposing also the global constraints. These configurations are briefly summarised in Figs.…”

Section: Resultsmentioning

confidence: 99%

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Fine‐grained recognition of maritime vessels and land vehicles by deep feature embedding

Solmaz

Gundogdu

Yücesoy

et al. 2018

IET Computer Vision

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Recent advances in large-scale image and video analysis have empowered the potential capabilities of visual surveillance systems. In particular, deep learning-based approaches bring in substantial benefits in solving certain computer vision problems such as fine-grained object recognition. Here, the authors mainly concentrate on classification and identification of maritime vessels and land vehicles, which are the key constituents of visual surveillance systems. Employing publicly available data sets for maritime vessels and land vehicles, the authors aim to improve visual recognition. Specifically, the authors focus on five tasks regarding visual recognition; coarse-grained classification, fine-grained classification, coarse-grained retrieval, fine-grained retrieval, and verification. To increase the performance in these tasks, the authors utilise a multi-task learning framework and present a novel loss function which simultaneously considers deep feature learning and classification by exploiting the available hierarchical labels of individual samples and the global statistics of distances between the data pairs. The authors observe that the proposed multi-task learning model improves the fine-grained recognition performance on MARVEL and Stanford Cars data sets, compared to training of a model targeting a single recognition task.

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Section: Resultsmentioning

confidence: 99%

“…It is also expected to experience a decrease in the possibility of over-fitting up to a certain degree. The overview of the losses in (8) and (17) are also illustrated in Fig. 3.…”

Section: Deep Metric Learningmentioning

confidence: 99%

Fine‐grained recognition of maritime vessels and land vehicles by deep feature embedding

Solmaz

Gundogdu

Yücesoy

et al. 2018

IET Computer Vision

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“…Alternatively, transfer learning can be utilised by fine-tuning through the entire network. AlexNet, Inception, and ResNet50 has been developed using the MARVEl dataset 16 , a large-scale image dataset for maritime vessels. MARVEl dataset is a huge collection of marine vessels consisting of 2 million images from ship photos and ship tracker website 17 .…”

Section: Related Workmentioning

confidence: 99%

Deep Convolutional Neural Network based Ship Images Classification

2021

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Ships are an integral part of maritime traffic where they play both militaries as well as non-combatant roles. This vast maritime traffic needs to be managed and monitored by identifying and recognising vessels to ensure the maritime safety and security. As an approach to find an automated and efficient solution, a deep learning model exploiting convolutional neural network (CNN) as a basic building block, has been proposed in this paper. CNN has been predominantly used in image recognition due to its automatic high-level features extraction capabilities and exceptional performance. We have used transfer learning approach using pre-trained CNNs based on VGG16 architecture to develop an algorithm that performs the different ship types classification. This paper adopts data augmentation and fine-tuning to further improve and optimize the baseline VGG16 model. The proposed model attains an average classification accuracy of 97.08% compared to the average classification accuracy of 88.54% obtained from the baseline model.

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“…However, this dataset lacks vessel trajectories and identifications of the vessels, so it cannot be used for training the re-ID network. A large dataset of vessels called MARVEL [ 32 ] containing two million vessel images, focuses on classification and is based on only cropped vessel images without context, so that it cannot be used for training a vessel detector because all vessels are located at image center without bounding boxes. A similar dataset focusing on the same harbour application and containing images from various maritime areas named VCA-VCO has recently been made publicly available by Ghahremani et al [ 33 ] (VCA-VCO dataset [ 33 ]: contact the authors to obtain a copy.).…”

Section: Vessel Datasetsmentioning

confidence: 99%

Multi-Camera Vessel-Speed Enforcement by Enhancing Detection and Re-Identification Techniques

Zwemer

Groot

Wijnhoven

et al. 2021

Sensors

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This paper presents a camera-based vessel-speed enforcement system based on two cameras. The proposed system detects and tracks vessels per camera view and employs a re-identification (re-ID) function for linking vessels between the two cameras based on multiple bounding-box images per vessel. Newly detected vessels in one camera (query) are compared to the gallery set of all vessels detected by the other camera. To train and evaluate the proposed detection and re-ID system, a new Vessel-reID dataset is introduced. This extensive dataset has captured a total of 2474 different vessels covered in multiple images, resulting in a total of 136,888 vessel bounding-box images. Multiple CNN detector architectures are evaluated in-depth. The SSD512 detector performs best with respect to its speed (85.0% Recall@95Precision at 20.1 frames per second). For the re-ID of vessels, a large portion of the total trajectory can be covered by the successful detections of the SSD model. The re-ID experiments start with a baseline single-image evaluation obtaining a score of 55.9% Rank-1 (49.7% mAP) for the existing TriNet network, while the available MGN model obtains 68.9% Rank-1 (62.6% mAP). The performance significantly increases with 5.6% Rank-1 (5.7% mAP) for MGN by applying matching with multiple images from a single vessel. When emphasizing more fine details by selecting only the largest bounding-box images, another 2.0% Rank-1 (1.4% mAP) is added. Application-specific optimizations such as travel-time selection and applying a cross-camera matching constraint further enhance the results, leading to a final 88.9% Rank-1 and 83.5% mAP performance.

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Generic and attribute-specific deep representations for maritime vessels

Cited by 12 publications

References 33 publications

Fine‐grained recognition of maritime vessels and land vehicles by deep feature embedding

Fine‐grained recognition of maritime vessels and land vehicles by deep feature embedding

Deep Convolutional Neural Network based Ship Images Classification

Multi-Camera Vessel-Speed Enforcement by Enhancing Detection and Re-Identification Techniques

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