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ORCID
https://orcid.org/0009-0003-7024-9602
Access Type
Campus-Only Access for Five (5) Years
Document Type
thesis
Degree Program
Electrical & Computer Engineering
Degree Type
Master of Science in Electrical and Computer Engineering (M.S.E.C.E.)
Year Degree Awarded
2023
Month Degree Awarded
September
Abstract
Semantic segmentation is a fundamental task in computer vision that aims to classify every pixel in an image into different categories. Deep convolutional neural networks (CNNs) have achieved state-of-the-art results in semantic segmentation. Deeplabv3+ is a deep CNN-based model that uses atrous convolution and a decoder network to improve the accuracy of semantic segmentation. In this research, we conduct an ablation study on Deeplabv3+ to analyze the importance of its different components and their impact on the performance of the model, which provides valuable insights for developing more efficient and accurate semantic segmentation models. Our study encompasses a comprehensive examination of Deeplabv3+. We explore its constituent elements, including the backbone network, the Atrous Spatial Pyramid Pooling (ASPP) module, and the decoder network. Our investigation delves into the reasons underlying performance changes resulting from the removal of these architectural components. This analysis provides a deeper understanding of their intrinsic roles in shaping the model’s segmentation efficacy. Notably, we identify that the backbone exerts a substantial impact. Changes to other components yield relatively minor effects, while modifications to the backbone wield a remarkable influence. The Encoder-decoder structure also bears significant weight, playing a pivotal role in the upsampling process. This structure significantly impacts precision, enhancing boundary clarity and positional accuracy. Moreover, we recognize the vital role of feature integration. Features aid in establishing pixel position information, enhancing boundary definition, and positioning accuracy. Furthermore, the ASPP module emerges as a critical factor. ASPP leverages multi-scale information to differentiate complex object boundaries, further enriching the model’s semantic understanding.
DOI
https://doi.org/10.7275/36006602.0
First Advisor
Tongping Liu
Second Advisor
Hui Guan
Third Advisor
Jeremy Gummeson
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.
Recommended Citation
Lei, Bowen, "Ablation Study on Deeplabv3+ for Semantic Segmentation" (2023). Masters Theses. 1332.
https://doi.org/10.7275/36006602.0
https://scholarworks.umass.edu/masters_theses_2/1332