Marine Object Detection Based on Kalman Filtering

Hwang, Jeoung Yeon; Pak, Sang-Hyon; Park, Gil-Yang

doi:10.6109/jicce.2011.9.3.347

Cited by 8 publications

(1 citation statement)

References 14 publications

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“…And 5 years later, Zhu et al [12] extracts vessels from spaceborne optical images by using a semisupervised hierarchical classification approach based on shape and grey intensity features. Hwang et al [13] integrated a visual camera with radar to detect MVs based on foreground and background segmentation using an average filter and Kalman filter. In 2013, Yang et al [14] used background sea surface features and a linear classifier to combine pixel and region characteristics to extract vessels.…”

Section: Related Workmentioning

confidence: 99%

MCMOD: The Multi-Category Large-Scale Dataset for Maritime Object燚etection

Sun¹,

Hu²,

Qi³

et al. 2023

Computers, Materials &Amp; Continua

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The marine environment is becoming increasingly complex due to the various marine vehicles, and the diversity of maritime objects poses a challenge to marine environmental governance. Maritime object detection technology plays an important role in this segment. In the field of computer vision, there is no sufficiently comprehensive public dataset for maritime objects in the contrast to the automotive application domain. The existing maritime datasets either have no bounding boxes (which are made for object classification) or cover limited varieties of maritime objects. To fulfil the vacancy, this paper proposed the Multi-Category Large-Scale Dataset for Maritime Object Detection (MCMOD) which is collected by 3 onshore video cameras that capture data under various environmental conditions such as fog, rain, evening, etc. The whole dataset consists of 16,166 labelled images along with 98,590 maritime objects which are classified into 10 classes. Compared with the existing maritime datasets, MCMOD contains a relatively balanced quantity of objects of different sizes (in the view). To evaluate MCMOD, this paper applied several state-of-the-art object detection approaches from computer vision research on it and compared their performances. Moreover, a comparison between MCMOD and an existing maritime dataset was conducted. Experimental results indicate that the proposed dataset classifies more types of maritime objects and covers more small-scale objects, which can facilitate the trained detectors to recognize more types of maritime objects and detect maritime objects over a relatively long distance. The obtained results also show that the adopted approaches need to be further improved to enhance their capabilities in the maritime domain.

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Section: Related Workmentioning

confidence: 99%

MCMOD: The Multi-Category Large-Scale Dataset for Maritime Object燚etection

Sun¹,

Hu²,

Qi³

et al. 2023

Computers, Materials &Amp; Continua

View full text Add to dashboard Cite

show abstract

A Four State Rotational Frequency Detector for Fast Frequency Acquisition

Yeo¹

2011

Journal of information and communication convergence engineerin

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This paper proposes a new rotational frequency detector (RFD) for phase-locked loop (PLL) or clock and data recovery (CDR) applications for fast frequency acquisition. The proposed RFD uses the four states finite state machine (FSM) model to accelerate the frequency acquisition time. It is modeled and simulated with MATLAB Simulink. The functionalities of the proposed RFD are examined and the results are compared to those of a conventional RFD. The proposed RFD's frequency acquisition time is four times faster than that of a conventional one. The proposed RFD incorporated with a phase detector (PD) in PLL or CDR is expected to improve the frequency and phase acquisition performance later greatly.

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Analysis of Doping Profile Dependent Threshold Voltage for DGMOSFET Using Gaussian Function

Jung¹

2011

Journal of information and communication convergence engineerin

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Marine Object Detection Based on Kalman Filtering

Cited by 8 publications

References 14 publications

MCMOD: The Multi-Category Large-Scale Dataset for Maritime Object燚etection

MCMOD: The Multi-Category Large-Scale Dataset for Maritime Object燚etection

A Four State Rotational Frequency Detector for Fast Frequency Acquisition

Analysis of Doping Profile Dependent Threshold Voltage for DGMOSFET Using Gaussian Function

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