A Lijiya

National Institute of Technology Calicut

Papers

Total Citations

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About

A. Lijiya is a researcher whose work bridges computer vision and real-time embedded systems, with a focus on efficient object detection and trajectory prediction. Her most notable contribution is the development of RFSOD, a lightweight single-stage detector designed for real-time embedded applications, specifically targeting the challenging task of detecting small-size objects. This work, which has garnered 17 citations, addresses a critical gap in deploying deep learning models on resource-constrained devices, making it highly relevant for applications like drones, surveillance, and autonomous systems. In a complementary vein, Lijiya has explored predictive modeling in sports technology, as seen in her paper on shuttlecock detection and fall point prediction using neural networks (6 citations). This research tackles the classic problem of moving object trajectory estimation in two dimensions, demonstrating her versatility in applying machine learning to dynamic environments. Her work is part of a broader effort to enhance real-time decision-making in embedded contexts, and her contributions are particularly valuable for students and researchers interested in efficient deep learning architectures, edge AI, and computer vision applications.