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An Optimized Segmentation Scheme for Ambiguous Pixels Based on Improved FCN and DenseNet

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Abstract

These past six years have witnessed that segmentation algorithms make a breakthrough due to the development of deep-based semantic segmentation networks, which can realize classification from image-level to pixel-level. However, when segmenting a small-batch, complex-features and obvious-gap database, these semantic segmentation networks will lead to segmentation ambiguities in some boundaries between foreground (i.e. the main segmentation body) and its background with limited image detail expression. To solve such problem, this paper proposes an optimized segmentation scheme for ambiguous pixels based on Atrous-ResFCN and MFR-DenseNet, namely ARMD. Firstly, masking algorithm and adaptive box mechanism are applied in pre-processed KITTI segmentation database to construct the classification database, which will be used to train ambiguities judgment model. In addition, Atrous-ResFCN-8s and Atrous-ResFCN-16s are proposed and further combined to determine segmentation ambiguities with different-level segmentation abilities. Finally, MFR-DenseNet is further migrated to optimize these ambiguous pixels with effective threshold selection. Experimental results demonstrate that our proposed ARMD algorithm is beneficial to improve segmentation accuracy, where the highest MIoU and PA are able to reach 88.18% and 95.88%, respectively.

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Data Availability Statement

Some or all data, models or code generated or used during this research are available from the corresponding author by request.

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Authors and Affiliations

  1. Fujian Key Lab for Intelligent Processing and Wireless Transmission of Media Information, College of Physics and Information Engineering, Fuzhou University, Fuzhou, Fujian, China

    Bolin Chen, Tiesong Zhao, Liping Zhou, Jing Yang, Jiahui Liu & Liqun Lin

  2. Peng Cheng Laboratory, Shenzhen, Guangdong, China

    Tiesong Zhao

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  1. Bolin Chen
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  2. Tiesong Zhao
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  3. Liping Zhou
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  4. Jing Yang
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  5. Jiahui Liu
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  6. Liqun Lin
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Correspondence to Liqun Lin.

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Chen, B., Zhao, T., Zhou, L. et al. An Optimized Segmentation Scheme for Ambiguous Pixels Based on Improved FCN and DenseNet. Circuits Syst Signal Process 41, 372–394 (2022). https://doi.org/10.1007/s00034-021-01784-9

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  • DOI: https://doi.org/10.1007/s00034-021-01784-9

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