Enhancing Image Segmentation Accuracy using Deep Learning Techniques

Authors

  • Kiran Sree Pokkuluri Department of C.S.E, Shri Vishnu Engineering College for Women, Vishnupur, Bhimavaram, Andhra Pradesh 534202, India
  • Usha Devi N. S.S.S.N Department of C.S.E, University College of Engineering, Jawaharlal Nehru Technology University, Kakinada, Andhra Pradesh 533003, India
  • Martin Margala School of Computing and Informatics, University of Louisiana at Lafayette, LA 70504, United States of America
  • Prasun Chakrabarti ITM (SLS) Baroda University, Vadodara, Gujarat 391510, India

DOI:

https://doi.org/10.37934/araset.49.1.139148

Keywords:

Deep learning, Image segmentation, CNN

Abstract

Accurate image segmentation is a fundamental task in computer vision with applications spanning from medical imaging to autonomous vehicles. This research paper introduces a novel approach for enhancing image segmentation accuracy through the utilization of deep learning techniques. Traditional segmentation methods often struggle with complex scenes, object occlusions, and varying lighting conditions. Leveraging the power of deep learning, we propose a custom convolutional neural network (CNN) architecture named DLwCA. This architecture incorporates advanced features such as residual connections and attention mechanisms to capture fine-grained details and contextual information. The proposed approach is evaluated on benchmark datasets and compared against established methods. Quantitative metrics including Intersection over Union (IoU) and F1-score demonstrate a significant improvement in segmentation accuracy. Our approach showcases a clear potential to revolutionize image segmentation tasks, offering precise delineation of object boundaries even in challenging scenarios. This research contributes to the growing body of knowledge on leveraging deep learning for advanced computer vision tasks and establishes a strong foundation for further research in the domain of image segmentation techniques. We have compared our work with the existing literature with various parameters like F1 Score, precision and accuracy. The proposed method reports an average accuracy of 91.9% and perming better than some baseline models.

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Author Biographies

Kiran Sree Pokkuluri, Department of C.S.E, Shri Vishnu Engineering College for Women, Vishnupur, Bhimavaram, Andhra Pradesh 534202, India

drkiransree@gmail.com

Usha Devi N. S.S.S.N , Department of C.S.E, University College of Engineering, Jawaharlal Nehru Technology University, Kakinada, Andhra Pradesh 533003, India

ushaucek@gmail.com

Martin Margala, School of Computing and Informatics, University of Louisiana at Lafayette, LA 70504, United States of America

drkspkkd@gmail.com

Prasun Chakrabarti, ITM (SLS) Baroda University, Vadodara, Gujarat 391510, India

akshsjna@gmail.com

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Published

2024-07-28

How to Cite

Pokkuluri, K. S., S.S.S.N , U. D. N., Margala, M., & Chakrabarti, P. (2024). Enhancing Image Segmentation Accuracy using Deep Learning Techniques. Journal of Advanced Research in Applied Sciences and Engineering Technology, 49(1), 139–148. https://doi.org/10.37934/araset.49.1.139148

Issue

Section

Articles