Cross-scale feature extraction module for efficient RGBD images semantic segmentation

Renyu Huang; Zhipeng Gao; Jianjia Zhang; Canrong Yao; Junyi Wu; Jianqiang Zhao

doi:10.1117/12.2626907

2 March 2022 Cross-scale feature extraction module for efficient RGBD images semantic segmentation

Renyu Huang, Zhipeng Gao, Jianjia Zhang, Canrong Yao, Junyi Wu, Jianqiang Zhao

Proceedings Volume 12158, International Conference on Computer Vision and Pattern Analysis (ICCPA 2021); 121580A (2022) https://doi.org/10.1117/12.2626907
Event: 2021 International Conference on Computer Vision and Pattern Analysis, 2021, Guangzhou, China

Abstract

Image semantic segmentation plays an important role in assisted driving systems and motor vehicle auto driving system. Due to the complexity of outdoor scenes and driving scenarios, algorithms that only use texture images have low robustness. In order to improve the performance of semantic segmentation, depth images can be used to assist texture images. In addition, the assisted driving system requires that the algorithm need to achieve real-time performance, but the existing algorithm is limited by the complexity of semantic segmentation, resulting in low operating efficiency. To address the above problems, a cross-scale feature extraction module for efficient RGBD image semantic segmentation is proposed. The cross-scale feature extraction module has the characteristics of small parameter amount, large receptive field, and the ability to merge multi-scale features, which can efficiently extract context features. The proposed model achieves a segmentation accuracy of 69.4% mIoU on the RGBD original resolution image of the outdoor scene dataset Cityscapes, and runs at a speed of up to 120 frames per second. Compared with related algorithms, the model proposed in this paper has obvious advantages in running speed, and has achieved a good balance between performance and efficiency.

Citation Download Citation

Renyu Huang, Zhipeng Gao, Jianjia Zhang, Canrong Yao, Junyi Wu, and Jianqiang Zhao "Cross-scale feature extraction module for efficient RGBD images semantic segmentation", Proc. SPIE 12158, International Conference on Computer Vision and Pattern Analysis (ICCPA 2021), 121580A (2 March 2022); https://doi.org/10.1117/12.2626907

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KEYWORDS

Image segmentation

RGB color model

Feature extraction

Convolution

Data modeling

Performance modeling

Computer programming

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