A Benchmark for Deep Learning Based Object Detection in Maritime Environments

Moosbauer, Sebastian; König, Daniel; Jäkel, Jens; Teutsch, Michael

doi:10.1109/cvprw.2019.00121

Cited by 67 publications

(40 citation statements)

References 46 publications

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“…The VAIS dataset consists of 1,623 visible images in six coarse-grained categories [13], consisting of merchant ship, sailing ship, medium passenger ship, medium "other" ship, tugboats, and small boat. To analyze the quality of the dataset it is necessary to check if the classes are equally distributed [6]. Fig.…”

Section: A the Vais Datasetmentioning

confidence: 99%

“…Large-scale annotated datasets train convolutional neural networks (CNNs) with supervised learning to achieve image classification in recent years [4]. However, the process of data training suffers considerable data-gathering effort and computational cost, and publicly available datasets are scarce in maritime environments [5], [6]. There are some maritime datasets, such as object detection and tracking [7], piracy detection [8], and obstacle detection [9], [10], but none of them is used for maritime classification [6].…”

Section: Introductionmentioning

confidence: 99%

“…However, the process of data training suffers considerable data-gathering effort and computational cost, and publicly available datasets are scarce in maritime environments [5], [6]. There are some maritime datasets, such as object detection and tracking [7], piracy detection [8], and obstacle detection [9], [10], but none of them is used for maritime classification [6]. Compared to the remote sensing datasets and infrared (IR) datasets [2], the electro-optical visible dataset is insufficiently applied to maritime environments [6], [7].…”

Section: Introductionmentioning

confidence: 99%

“…There are some maritime datasets, such as object detection and tracking [7], piracy detection [8], and obstacle detection [9], [10], but none of them is used for maritime classification [6]. Compared to the remote sensing datasets and infrared (IR) datasets [2], the electro-optical visible dataset is insufficiently applied to maritime environments [6], [7]. The majority of maritime visible classification datasets is closed source to difficultly obtain, such as the Maritime Detection Classification and Tracking (MarDCT) datasets [11] and E2S2-Vessel dataset [12].…”

Section: Introductionmentioning

confidence: 99%

“…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]. In contrast to the two publicly available datasets, few datasets merge different existing annotated maritime datasets and unlabelled data in maritime environments for image classification.…”

Section: Introductionmentioning

confidence: 99%

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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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Section: A the Vais Datasetmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Maritime Visible Image Classification Based on Double Transfer Method

et al. 2020

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Field experiment of autonomous ship navigation in canal and surrounding nearshore environments

Kim,

Lee,

Chung

et al. 2023

Journal of Field Robotics

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In this paper, we present the development of autonomous navigation capabilities for small cruise boats, and their verification by field experiments in a canal and its surrounding waters. A cruise boat was converted to an autonomous surface vehicle (ASV) by installing various sensors and actuators to enable autonomous navigation. Navigation and perception sensors, such as global positioning system, attitude and heading reference system, radar, light detection and ranging (LiDAR), and cameras, were mounted on the ASV to estimate its motion and perceive the surrounding environment. Motors and potentiometers were installed for active control of the ASV. Software system components including navigation filters, object‐detection, path‐planning, and control algorithms were designed and implemented. In the narrow canal region, LiDARs were used to detect the side walls and boundaries of the canal. In open areas outside the canal, obstacles and object features were detected using various combinations of onboard sensors. A model‐based path‐planning algorithm was designed to avoid the detected obstacles, and the line‐of‐sight guidance was employed to control the vehicle. The performance of the developed system was verified through a field experiment in a real‐world maritime environment.

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