A Deep-Learning-Based Lightweight Model for Ship Localizations in SAR Images

A Deep-Learning-Based Lightweight Model for Ship Localizations in SAR Images

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Product Code: Python - Deep Learning
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Product Description

Aim:

To automate the detection of presence of ships and to classify the types of ships available in the given Image.


Introduction:

Ship detection has broad applications in many areas, including fishery management, maritime rescue, and maritime monitoring. Recently, numerous detectors based on deep learning have been carried in ship detection in synthetic aperture radar (SAR) images. However, detecting the inshore ships faces enormous challenges because of the strong scattering interference of the inland area. In order to address such issues, a novel method named strong scattering points network for ship detection is proposed in this article. First, according to the SAR imaging mechanism, the ships usually appear strong scattering phenomenon in the SAR images. Therefore, the proposed method detects the strong scattering points on the ship and then aggregates their positions to obtain the ship’s arbitrary orientation box. Second, our method designs an embedding vector to cluster these points as an individual object to regress the oriented bounding box. Third, in order to distinguish the strong scattering points on land, a ship attention module is employed to extract the image texture features and representations of local features. It can suppress the false alarm caused by land interference in the detection process.


Synopsis:

The detection of inshore and offshore ships is an essential task for a large variety of applications in both military and civilian fields. For example, in the civil field, ship detection plays a strong supervisory role in monitoring and managing marine traffics, transportation, fisheries dumping of pollutants and illegal smuggling. The dataset of sea ships images where trained and used to detect the presence of the ships in a given image.


Proposed System:

A system is proposed to automate the detection of presence of ships in the given image using Machine Learning and Deep Learning Algorithms. We are proposing along with ship detection, a ship classification based on the type and category of the ships. The proposed system will not only detect a ship but also categorize as war ship, container ship etc.



Advantage:


In recent years, various methods of SAR ship detection have been proposed and remarkable progress has been made. However, ship detection has two major problems. First, only a few methods are proposed for oriented ship. detection. The traditional ship detection methods and deep learning based approaches utilize the horizontal bounding box, which cannot fit the narrow and long ship well and is difficult to reflect the direction information of the ship. 


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Software Projects Includes

  1. Demo  Video
  2. Abstract
  3. Base paper
  4. Full Project PPT
  5. UML Diagrams
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  7. Source Code
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  10. Reference Papers
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  1. Demo  Video
  2. Abstract
  3. Base paper
  4. Full Project PPT
  5. Datasheets
  6. Circuit Diagrams
  7. Source Code
  8. Screen Shots & Photos
  9. Software Links
  10. Reference Papers
  11. Lit survey
  12. Full Project Documentation
  13. Online support


The Delivery time for Hardware projects is 7-8 working days.

   

Mini Projects: Software Includes

  1. Demo  Video
  2. Abstract
  3. Base paper
  4. Full Project PPT
  5. UML Diagrams
  6. SRS
  7. Source Code
  8. Screen Shots
  9. Software Links
  10. Reference Papers
  11. Full Project Documentation
  12. Online support

 

The Delivery time for software Miniprojects is 2 -3 working days.

 

Mini Projects - Hardware includes

  1. Demo  Video
  2. Abstract
  3. PPT
  4. Datasheets
  5. Circuit Diagrams
  6. Source Code
  7. Screen Shots & Photos
  8. Software Links
  9. Reference Papers
  10. Full Project Documentation
  11. Online support

The Delivery time for Hardware Mini projects is 7-8 working days.