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.