M. Arshath

Kalasalingam Academy of Research and Education

Papers

Total Citations

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About

M. Arshath is an emerging researcher in computer vision and deep learning, with a focused interest in object detection for real-world applications. His most-cited work, "Object Detection on Traffic Data Using Yolo" (2023), has garnered 4 citations, demonstrating early impact in applying state-of-the-art detection algorithms to traffic surveillance and autonomous driving contexts. Arshath’s research centers on improving the accuracy and efficiency of object localization in complex visual scenes, particularly using the YOLO (You Only Look Once) framework. By adapting these models to traffic data, he addresses critical challenges in real-time vehicle and pedestrian detection, contributing to safer and more intelligent transportation systems. His work bridges the gap between theoretical computer vision and practical deployment, offering insights into how deep learning can handle variable lighting, occlusion, and dense traffic environments. As a rising voice in applied AI, Arshath’s contributions are paving the way for more robust and responsive vision-based technologies in smart city infrastructure.