Sindhu Drishti

Advanced Deep Learning for Precise Ship Detection in Naval Environments

This project proposes to

Enhance naval surveillance & intelligence operations

Our platform provides an automated, reliable solution for detecting and classifying various ship categories from periscope-view images, tailored specifically for the challenging conditions of naval environments.

Automated Ship Detection

Leveraging advanced deep learning algorithms, Sindhu Drishti delivers precise ship detection capabilities, enabling users to identify different types of vessels with accuracy.

Annotation and Refinement

Sindhu Drishti includes an integrated annotation tool that allows users to refine detection results. This feature empowers users to enhance the system’s accuracy by providing feedback and correcting detections.

Image Processing

Users can effortlessly upload and process images, with the system performing ship detection swiftly and efficiently.

Adaptive Model Retraining

The system is designed to adapt and improve over time. Users can initiate retraining of the detection model based on newly processed images and feedback, ensuring continuous enhancement of detection performance.

USE
CASES

AI Based Object Detection

The application utilizes advanced AI algorithms to perform object detection on naval ships, categorizing them into various classes .

Edge Computing Devices

It integrates the AI algorithm with existing infrastructure at the customer’s end through edge computing devices, ensuring efficient processing and analysis of data

Real-Time Object Detection

The application offers real-time object detection capabilities, enabling immediate identification and analysis of objects within the naval environment..

Project Applications

Expanded Real-Time Detection Tasks

The edge system has the potential to extend its capabilities beyond naval object detection, encompassing major real-time detection tasks such as quality control and object counting across various industries.

Infrastructure-Level System Setup

The application can evolve into an infrastructure-level system setup capable of handling real-time video feeds from multiple sources. This enables advanced functionalities such as multi-object tracking, access control within organizations, and numerous other computer vision-based applications.

Functionalities

This system would be a valuable asset to submarine crew

  • Object Detection and Classification
  • Deep Stream Integration
  • Real-time Visualization
  • Wireless Data Transfer
  • Distance Calculation
  • Database Integration

Benefits

The development of this system would have a number of benefits for submarine crews.

Increased situational awareness

The system would allow submarine crews to identify potential threats earlier, giving them more time to react.

Improved decision-making

The system would provide submarine crews with more accurate information about the types of ships in the area, which would help them to make better decisions about how to respond to threats.

Enhanced safety

The system would help to reduce the risk of submarine crews being detected by surface ships.

CO-developed By