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Efficiency in constraint: A comparative analysis review of FCN and DeepLab models on small-scale datasets
- 1 Shanghai Jiao Tong Universiy
* Author to whom correspondence should be addressed.
Abstract
Semantic segmentation, crucial in computer vision, differentiates objects within images and finds applications in autonomous vehicles, medical imaging, and assistive technology. It typically employs neural networks for pixel-wise image classification. Key advancements in this field are attributed to Fully Convolutional Networks (FCN) and DeepLab models, known for their high performance with extensive datasets. However, the challenge arises when these models are applied to smaller datasets. Our research presents a concise overview of seminal work in machine learning and semantic segmentation, followed by an exploration of FCN and DeepLab architectures. The study primarily focuses on evaluating these models’ efficacy on a smaller dataset. Results, summarized in tables and figures, indicate that FCN-16 outperforms others in limited-data scenarios, while DeepLab shows reduced effectiveness. This finding is significant for applications with constrained data resources, providing a direction for future research in semantic segmentation under such conditions.
Keywords
FCN, DeepLab, CRF
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Cite this article
Yang,H. (2024). Efficiency in constraint: A comparative analysis review of FCN and DeepLab models on small-scale datasets. Applied and Computational Engineering,75,19-30.
Data availability
The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.
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Volume title: Proceedings of the 2nd International Conference on Software Engineering and Machine Learning
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