PLANETORY BODIES IDENTIFICATION

COMPUTER ENGINEERING

Identifying celestial objects in space is a complex task, traditionally requiring specialized equipment and expertise. This project introduces a comprehensive solution using advanced deep learning techniques to automate the identification process of planetary bodies, including planets, asteroids, moons, and more. By employing the Inception v3 model for classification and the MobileNet model for detection, the system achieves high accuracy and efficiency in categorizing and localizing celestial objects within input images. To make this technology accessible to a wider audience, a user-friendly web application interface is developed using HTML, CSS, JavaScript, and Flask. This interface allows users to easily upload images through their web browser and receive instant feedback on the identified celestial objects. Upon submission, the system processes the image using the integrated deep learning models and provides users with detailed information about the identified objects, including their history, physical characteristics, and relevant astronomical data. This integration not only enhances the accuracy and efficiency of celestial object identification but also democratizes access to astronomical knowledge, empowering amateur astronomers, educators, and space enthusiasts to explore and understand the universe.

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