CNN-based object detection and segmentation for maritime domain awareness

Nita, Cornelia; Vandewal, Marijke

doi:10.1117/12.2573287

Cited by 8 publications

(7 citation statements)

References 11 publications

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“…[20], [21], [22], [23] applied Mask RCNN to detect merchant ships and marine buoys on sea. [24], [25], [26] detected warships by applying Mask RCNN and YOLO. However, [24], [25], [26] has its limitation in being only capable of detecting warships, and not being able to classify the class of warship nor distinguish between mounted guided missiles and guns.…”

Section: Related Work a Maritime Object Detectionmentioning

confidence: 99%

“…[24], [25], [26] detected warships by applying Mask RCNN and YOLO. However, [24], [25], [26] has its limitation in being only capable of detecting warships, and not being able to classify the class of warship nor distinguish between mounted guided missiles and guns. To reduce the weight of Mask RCNN, the base backbone was replaced with Mo-bileNet V1 [27] and the number of convolutional computation kernels of the head was reduced in [28].…”

Section: Related Work a Maritime Object Detectionmentioning

confidence: 99%

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Lightweight Mask RCNN for Warship Detection and Segmentation

Park

Moon

2022

IEEE Access

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As the term X(Everything)+AI indicates, AI is applied in every aspect of current societies. Likewise, the military requirements for AI are increasing as well. AIs that automatically detect and classify objects are required for surveillance and reconnaissance. Especially in terms of naval operation, identifying types of warships and recognizing mounted armaments have significance as the first step of the operation. This study is the proposal of an AI model that can identify warships' type and weapon by analyzing video information taken on sea, and evaluate threat priority and response level. The proposed model is based on Mask RCNN, the Image Segmentation model, but was designed in a lightweight version, so that it could be used on a platform of the vessel where the use of high performing computers is limited. To lightweight the model, the former backbone was replaced with MobileNetV2, and the convolution operation of the RPN was replaced with Depthwise Separable Convolution operation, which operates respectively in each channel. The lightweight Mask RCNN model showed 64% lower number of parameters compared to the base model. However, its mAP, the classification accuracy, was similar with the base model.

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Section: Related Work a Maritime Object Detectionmentioning

confidence: 99%

Section: Related Work a Maritime Object Detectionmentioning

confidence: 99%

Lightweight Mask RCNN for Warship Detection and Segmentation

Park

Moon

2022

IEEE Access

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“…Visible Chen [12], Cane [13], Marie [14], Chen [15], Chen [16], Liu [17], Shan [18], Gal [19], Lin [20], Chen [21], Lee [22], Feng [23], Shan [24], Fefilatyev [25] IR N/A Tang [26], Liu [27], Hu [28], Lin [29] MWIR Özertem [30], Wang [31] LWIR Sun [32], Lu [33], Bai [34], Leira [35], Bai [36], Mumtaz [37], Singh [38], Zhang [39], Xu [40], Zhou [41], Schöller [42], Li [43], Westlake [44] Visible + IR N/A Islam [45], Wei [46] Visible + MWIR Nita [47] Visible + LWIR Zhang [48], Ribeiro [49], Farahnakian [50] Visible + SWIR + LWIR Stets [51] Visible + SWIR + MWIR + LWIR Bouma [52] Visible: refers to visible-band images; IR: infrared images, including short-wave infrared (SWIR), medium-wave infrared (MWIR), and long-wave infrared (LWIR); N/A, means that the type of image was not specified in the paper. from the statistical data that infrared images are used the most, followed by visible images.…”

Section: Types Of Electro-optical Images Articlesmentioning

confidence: 99%

“…Chen [15], Shan [24] Other Others Gal [88], Farahnakian [50], Liu [27], Cane [13], Nita [47] Many researchers have used one-stage detectors. In ship detection, the detection speed needs to be considered.…”

Section: One-stage and Two-stage Detectors In Ship Detectionmentioning

confidence: 99%

A Review of Methods for Ship Detection with Electro-Optical Images in Marine Environments

Wang

Fan

et al. 2021

JMSE

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The ocean connects all continents and is an important space for human activities. Ship detection with electro-optical images has shown great potential due to the abundant imaging spectrum and, hence, strongly supports human activities in the ocean. A suitable imaging spectrum can obtain effective images in complex marine environments, which is the premise of ship detection. This paper provides an overview of ship detection methods with electro-optical images in marine environments. Ship detection methods with sea–sky backgrounds include traditional and deep learning methods. Traditional ship detection methods comprise the following steps: preprocessing, sea–sky line (SSL) detection, region of interest (ROI) extraction, and identification. The use of deep learning is promising in ship detection; however, it requires a large amount of labeled data to build a robust model, and its targeted optimization for ship detection in marine environments is not sufficient.

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“…Existing works evaluate object detection methods in maritime environments on their private ship detection datasets [ 19 , 20 ]. Nita et al, in [ 21 ], tackle the task of ship instance segmentation without a real-time approach, using only Mask-RCNN [ 15 ] on their private dataset. A comparison of state-of-the-art ship instance segmentation methods from maritime oblique view images, with a focus on robust and real-time methods, and a public dataset, has not been found.…”

Section: Introductionmentioning

confidence: 99%

Ship Segmentation and Georeferencing from Static Oblique View Images

Carrillo-Perez¹,

Barnes²,

Stephan³

2022

Sensors

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Camera systems support the rapid assessment of ship traffic at ports, allowing for a better perspective of the maritime situation. However, optimal ship monitoring requires a level of automation that allows personnel to keep track of relevant variables in the maritime situation in an understandable and visualisable format. It therefore becomes important to have real-time recognition of ships present at the infrastructure, with their class and geographic position presented to the maritime situational awareness operator. This work presents a novel dataset, ShipSG, for the segmentation and georeferencing of ships in maritime monitoring scenes with a static oblique view. Moreover, an exploration of four instance segmentation methods, with a focus on robust (Mask-RCNN, DetectoRS) and real-time performances (YOLACT, Centermask-Lite) and their generalisation to other existing maritime datasets, is shown. Lastly, a method for georeferencing ship masks is proposed. This includes an automatic calculation of the pixel of the segmented ship to be georeferenced and the use of a homography to transform this pixel to geographic coordinates. DetectoRS provided the highest ship segmentation mAP of 0.747. The fastest segmentation method was Centermask-Lite, with 40.96 FPS. The accuracy of our georeferencing method was (22±10) m for ships detected within a 400 m range, and (53±24) m for ships over 400 m away from the camera.

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CNN-based object detection and segmentation for maritime domain awareness

Cited by 8 publications

References 11 publications

Lightweight Mask RCNN for Warship Detection and Segmentation

Lightweight Mask RCNN for Warship Detection and Segmentation

A Review of Methods for Ship Detection with Electro-Optical Images in Marine Environments

Ship Segmentation and Georeferencing from Static Oblique View Images

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