Walk the Lines: Object Contour Tracing CNN for Contour Completion of Ships

Kelm, André Peter; Zölzer, Udo

doi:10.1109/icpr48806.2021.9412410

Cited by 3 publications

(1 citation statement)

References 34 publications

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“…Ust et al [15] introduced a scaffolding learning regime (SLR) that trained an obstacle detection segmentation network under weak supervision for individual ship contour extraction. Kelm et al [16] trained a CNN to identify central pixels; the network recognized a part of an input image and calculated a rotation angle, and used the central pixel to describe the upcoming directional change in the contour. Deep learning methods that rely on training data are more accurate than other established methods, and are highly adaptable to different scenarios.…”

Section: Introductionmentioning

confidence: 99%

Online Estimation of Ship Dimensions by Combining Images with AIS Reports

Huang,

Hu,

et al. 2023

JMSE

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Ship dimensions are an important component of static AIS information, and are a key factor in identifying the risks of ship collisions. We describe a method of extracting and correcting ship contour information using inland waterway surveillance video combined with AIS information that does not depend on ship dimension data. A lightweight object detection model was used to determine the ship’s position in an image. Dynamic AIS information was included to produce multigroup control points, solve the optimal homography matrix, and create a transformation model to map image coordinates onto water surface coordinates. A semantic segmentation DeepLabV3+ model was used to determine ship contours from the images, and the actual dimensions of the ship contours were calculated using homography matrix transformation. The mAP of the proposed object detection model and the MIoU of the semantic segmentation model were 86.73% and 91.07%, respectively. The calculation error of the ship length and width were 5.8% and 7.4%, respectively. These statistics indicate that the proposed method rapidly and accurately detected target ships in images, and that the model estimated ship dimensions within a reasonable range.

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Section: Introductionmentioning

confidence: 99%

Online Estimation of Ship Dimensions by Combining Images with AIS Reports

Huang,

Hu,

et al. 2023

JMSE

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Algorithm for the formation of closed contours of objects represented by small piecewise-discontinuous functions for the tasks of constructing stable features and automated selection of areas of complex shape when processing scenes obtained in the infrared and optical ranges

Semenishchev

Voronin²,

Zelensky³

et al. 2022

Signal Processing, Sensor/Information Fusion, and Target Recognition XXXI

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Literature Review on Ship Localization, Classification, and Detection Methods Based on Optical Sensors and Neural Networks

Teixeira

Araujo

Costa

et al. 2022

Sensors

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Object detection is a common application within the computer vision area. Its tasks include the classic challenges of object localization and classification. As a consequence, object detection is a challenging task. Furthermore, this technique is crucial for maritime applications since situational awareness can bring various benefits to surveillance systems. The literature presents various models to improve automatic target recognition and tracking capabilities that can be applied to and leverage maritime surveillance systems. Therefore, this paper reviews the available models focused on localization, classification, and detection. Moreover, it analyzes several works that apply the discussed models to the maritime surveillance scenario. Finally, it highlights the main opportunities and challenges, encouraging new research in this area.

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Walk the Lines: Object Contour Tracing CNN for Contour Completion of Ships

Cited by 3 publications

References 34 publications

Online Estimation of Ship Dimensions by Combining Images with AIS Reports

Online Estimation of Ship Dimensions by Combining Images with AIS Reports

Algorithm for the formation of closed contours of objects represented by small piecewise-discontinuous functions for the tasks of constructing stable features and automated selection of areas of complex shape when processing scenes obtained in the infrared and optical ranges

Literature Review on Ship Localization, Classification, and Detection Methods Based on Optical Sensors and Neural Networks

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