TITLE:
Deep Learning Based Semantic Image Segmentation: A Problem Driven Analysis of Architectures, Datasets, and Open Challenges
AUTHORS:
Mehadi Hasan, Amina Khatun, Sadia Afrin Juie, Sumaita Binte Shorif, Mohammad Shorif Uddin
KEYWORDS:
Semantic Segmentation, Convolutional Neural Network, Weakly Supervised Method, Deep Learning, Computer Vision
JOURNAL NAME:
Journal of Computer and Communications,
Vol.14 No.7,
July
9,
2026
ABSTRACT: Semantic segmentation is a cornerstone of computer vision, playing a crucial role in scene understanding and object recognition. The main goal of semantic segmentation is to give each pixel in an image its own label breaking it up into meaningful areas. This method helps machines understand visual data better allowing them to look at and make sense of images at a higher level. This paper presents a comprehensive, problem-driven analysis of deep learning based semantic image segmentation, focusing on architectures, datasets, evaluation metrics, and open challenges. It emphasizes key problem domains such as multi-scale context modeling, boundary accuracy, computational efficiency, and data scarcity. We analyze widely used deep learning methods, including Fully Convolutional Networks (FCNs), DeepLab, SegNet, and recurrent-based models, highlighting their strengths and limitations in addressing these challenges. We provide a structured overview of benchmark datasets and discuss evaluation metrics such as Intersection over Union (IoU) and pixel accuracy in practical contexts. A meta-level comparative analysis is conducted to examine trade-offs between accuracy, efficiency, and generalization across different approaches. Furthermore, we propose a conceptual segmentation framework integrating attention mechanisms, multi-scale feature extraction, and feature fusion. Finally, we identify current research gaps and outline future directions, including real-time segmentation, multi-modal learning, and data-efficient approaches.