Yahia Said

University of Monastir, Northern Border University

Papers

Total Citations

140

H-Index

About

Yahia Said is a leading researcher in applied deep learning, with a primary focus on indoor scene understanding, object detection, and robotic navigation. His work bridges computer vision and artificial intelligence to create practical solutions for autonomous systems. Said’s most influential contribution is his 2020 paper, “Deep Learning Based Application for Indoor Scene Recognition,” which has garnered 76 citations and established a robust framework for classifying complex indoor environments. He further advanced this domain with his 2019 study on indoor image recognition using deep convolutional neural networks (39 citations), demonstrating high accuracy in real-world settings. More recently, his 2022 evaluation of EfficientDet for object detection in indoor robot navigation (25 citations) has provided critical insights for developing efficient, real-time assistance systems. Said’s research is notable for its direct impact on robotics and smart environments, offering scalable models that enhance machine perception in cluttered, dynamic spaces. With a growing citation record and a focus on deployable AI, Yahia Said continues to shape the future of indoor scene analysis and autonomous navigation.