Sabeena Beevi K

Work place: Department of Electrical and Electronics Engineering, TKM College of Engineering, Kollam, Kerala, India



Research Interests: Computational Biology, Biology


Dr. Sabeena Beevi K is the Head of the Department of Electrical and Electronics Engineering, T K M College of Engineering, Kollam, Kerala, India. She received the Ph.D. degree from the University of Kerala, in 2018 in Electrical and Electronics Engineering and her M.Tech. Degree in computer science with specialization in Digital Image Computing in 2009 from the Computer Science Department of Kerala University. In June 1998, she joined the Electrical & Electronics Engineering Department at T K M College of Engineering, where she is currently working as an Associate Professor. Her research interests include pattern recognition, machine learning, medical image analysis and AI applications in Electrical engineering. She bagged the Best Thesis Award 2019 from the IEEE Communication Society through the ‘GATE’7’ contest. In 2021 she received Guidence Award for the Best Project from 5th National Level IEEE Project Competition. She serves as Reviewer of IEEE Journal of Biomedical and Health Informatics, IEEE EMBS, IEEE Access, Reviewer of SPICES’17, NetACT’19 Springer Conference, ComNet’20 Springer Conference, RAICS 2020 and SPICES 2022 IEEE Conferences. She has published several papers in international journals and conferences. She is a senior member of IEEE, IE (I), IEEE Engineering in Medicine & Biology Society and Computer Society of India (CSI).

Author Articles
Advancing Road Scene Semantic Segmentation with UNet-EfficientNetb7

By Anagha K J Sabeena Beevi K

DOI:, Pub. Date: 8 Dec. 2023

Semantic segmentation is an essential tool for autonomous vehicles to comprehend their surroundings. Due to the need for both effectiveness and efficiency, semantic segmentation for autonomous driving is a difficult task. Present-day models’ appealing performances typically come at the cost of extensive computations, which are unacceptable for self-driving vehicles. Deep learning has recently demonstrated significant performance improvements in terms of accuracy. Hence, this work compares U-Net architectures such as UNet-VGG19, UNet-ResNet101, and UNet-EfficientNetb7, combining the effectiveness of compound-scaled VGG19, ResNet101, and EfficientNetb7 as the encoders for feature extraction. And, U-Net decoder is used for regenerating the fine-grained segmentation map. Combining both low-level spatial information and high-level feature information allows for precise segmentation. Our research involves extensive experimentation on diverse datasets, including the CamVid (Cambridge-driving Labeled Video Database) and Cityscapes (a comprehensive road scene understanding dataset). By implementing the UNet-EfficientNetb7 architecture, we achieved notable mean Intersection over Union (mIoU) values of 0.8128 and 0.8659 for the CamVid and Cityscapes datasets, respectively. These results outshine alternative contemporary techniques, underscoring the superior precision and effectiveness of the UNet-EfficientNetb7 model. This study contributes to the field by addressing the crucial challenge of efficient yet accurate semantic segmentation for autonomous driving, offering insights into a model that effectively balances performance and computational demands.

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