Enhancing precision and efficiency: small object segmentation in high-resolution images using multi-task networks

Kaiwen Bian; Zhengguang Xu

doi:10.1117/12.3029186

22 May 2024 Enhancing precision and efficiency: small object segmentation in high-resolution images using multi-task networks

Kaiwen Bian, Zhengguang Xu

Author Affiliations +

Proceedings Volume 13176, Fourth International Conference on Machine Learning and Computer Application (ICMLCA 2023); 131760L (2024) https://doi.org/10.1117/12.3029186
Event: Fourth International Conference on Machine Learning and Computer Application (ICMLCA 2023), 2023, Hangzhou, China

Abstract

Semantic segmentation is a typical task in computer vision. To address the issue of poor performance for small objects in high-resolution images by existing semantic segmentation networks, a method based on a multi-task network is proposed in this paper. This approach jointly learns relevant tasks, enhancing the accuracy of small object segmentation. The methodology of upsampling in semantic segmentation has been refined through the amalgamation of sub-pixel convolution and conventional convolution techniques, ensuring the meticulous preservation of intricate details. Additionally, a sophisticated post-processing module has been introduced, effectively enhancing the accuracy of small object edge segmentation. The empirical results highlight the effectiveness of this approach, significantly enhancing the segmentation quality of small objects in high-resolution images, while simultaneously ensuring real-time computational efficiency.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

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Kaiwen Bian and Zhengguang Xu "Enhancing precision and efficiency: small object segmentation in high-resolution images using multi-task networks", Proc. SPIE 13176, Fourth International Conference on Machine Learning and Computer Application (ICMLCA 2023), 131760L (22 May 2024); https://doi.org/10.1117/12.3029186

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KEYWORDS

Image segmentation

Semantics

Object detection

Convolution

Education and training

Feature extraction

Network architectures

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