Authors : Gauri Vani Todur

Volume/Issue : Volume 11 - 2026, Issue 6 - June

Google Scholar : https://tinyurl.com/5xhepd99

Scribd : https://tinyurl.com/2e6khdf3

DOI : https://doi.org/10.38124/ijisrt/26jun1572

Note : A published paper may take 4-5 working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and ResearchGate.

Abstract : Near Earth asteroids (NEAs) are defined as minor planets with orbits close to Earth that could pose potential collision risks. Over 98% of the estimated 3 million small near-Earth asteroids (19-140 meters) remain undiscovered due to their faintness, leaving Earth vulnerable to impacts like the undetected 18-meter-sized 2013 Chelyabinsk meteor that caused injuries to ∼1600 people. The purpose of this research is to develop an automated deep learning-based pipeline to accurately discover small, faint, near-Earth asteroids. Because of its ability to pick up faint thermal signals, archival image data in the W2 band from the Near-Earth Object Wide-Field Infrared Survey Explorer (NEOWISE) was used. First, I filtered out stationary objects using a star masking process, and bright artifact pixel patterns by referencing WISE archival bitmask frames.

Keywords : Near-Earth Asteroids, Convolutional Neural Network, NEOWISE, Deep Learning, Infrared Astronomy, Moving Object Detection, Planetary Defense, Synthetic Image Generation, Asteroid Tracklet Linking.

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