Ship Draft Detection Based on Machine Vision RAN Xin, SHI Chaojian, - - PowerPoint PPT Presentation

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Ship Draft Detection Based on Machine Vision RAN Xin, SHI Chaojian, - - PowerPoint PPT Presentation

Feb 22, 2023 211 likes •395 views

Ship Draft Detection Based on Machine Vision RAN Xin, SHI Chaojian, XIAO Baojia Merchant Marine College, Shanghai Maritime University, Shanghai, P.R. China 2012-10-2 1 Introduction Water-borne vessels can carry large amounts of cargo

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Ship Draft Detection Based on Machine Vision

RAN Xin, SHI Chaojian, XIAO Baojia

Merchant Marine College, Shanghai Maritime University, Shanghai, P.R. China 2012-10-2

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1 Introduction

  • Water-borne vessels

can carry large amounts of cargo economically

  • It is important to
  • btain accurate

readings of the vessel draft to determine the amount of cargo that has been loaded onto the vessel.

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1 Introduction

  • The ship draft marks

are located at 6 specific positions around the freeboard.

  • The marine surveyors

will observe the draft lines and read the numbers before and after unloading cargoes, then use them to calculate the weight of cargoes.

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1 Introduction

  • Limits of draft

survey by manual

  • bservation

– Subjective visual estimation leads to different results – Conditions on

  • ceans and rivers

can drastically affect the draft line measurements

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2 Draft survey by machine vision

undetected detected Preprocessing Draft line detection enhancement Ship draft calculation Original ship draft image

Draft mark recognition

Image acquisition

Recognition

Result statistic and display Draft detection

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2.1 Image acquisition

  • The original images are taken by surveyor around the ship using camera,

then the image data are transferred to the computer to process.

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2.1 Image acquisition

  • Usually not suitable for direct detection of draft line due to

inappropriate position or view angle of surveyor, and also due to the influence of sunshine or wave conditions.

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2.2 Image preprocessing

  • The red, green and blue channel are divided from the
  • riginal image. It is noticed that the draft line is more

distinct in red channel than in other channels.

  • So the red channel will be split from the original image

and used at the subsequently step.

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2.3 Edge detection

  • The results illustrate that the best way to extracting

draft line is Canny operator adopted in red image channel.

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2.4 Geometry transformation

  • An affine transform algorithm is used to adjust

the image making the draft line horizontal.

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2.5 Hough transform

  • The two longer lines, the draft line and the

upper waterline, are detected and illustrated in green.

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2.6 Draft line detection

  • Depending on the common sense that the watermark

line is always at upper position than draft line, the lower and true draft line will be picked out at the final step.

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  • 3. Draft mark recognition
  • Binarization
  • Draft mark

extraction

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  • 3. Draft mark recognition –continue
  • Thin algorithm
  • f mathematical

morphology.

  • Draft mark

recognition based on trigeminal point features.

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  • 3. Draft mark recognition –continue
  • Draft mark

calculation and display.

  • Draft mark

statistic.

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  • 4. Conclusion
  • Draft line detection is the first and significant

step for ship draft survey.

  • In order to overcome the limits of the traditional

ship draft survey methods, an automatic recognition system based on machine vision is presented.

  • The experimental results show that the

proposed system is effective and can be used instead of visual observation.

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Thank You!