改进并行双分支结构的实时性语义分割算法研究

doi:10.3778/j.issn.1002-8331.2310-0034

摘要/Abstract

摘要： 实时性语义分割由于其轻量化的网络和较快的推理速度在智能驾驶的道路场景中具有重要的应用价值。为解决道路场景中小目标信息丢失和细节被上下文淹没问题，提出了并行双分支结构的DDRPNet模型。设计了PAPPM模块融合不同尺度的语义边缘特性，增强对边界信息的建模能力。在低分辨率分支的1/16、1/32和1/64分辨率特征图后加入坐标注意力机制，以捕获不同尺度下的位置信息和通道信息，填补小目标信息丢失问题。算法在Cityscapes数据集上以46.3 FPS的实时性表现达到了mIoU为76.28%的准确性；在CamVid数据集以95.2 FPS的实时性表现达到了mIoU为73.2%的准确性。实验结果表明，该模型在精度和速度上达到良好平衡，语义分割性能显著提升，在智能驾驶领域有潜在应用前景。

关键词: 实时性语义分割, 双分支结构, 坐标注意力机制, 智能驾驶

Abstract: In order to solve the problem of losing small target information and details being flooded by context in road scenes, a DDRPNet model with parallel two-branch structure is proposed. The proposed DDRPNet has two noticeable features. Firstly, the PAPPM module is introduced to fuse the semantic edge features at different scales. Secondly, a coordinate attention mechanism is added after the 1/16, 1/32 and 1/64 resolution feature maps of the low-resolution branch to capture the position and channel information at different scales and fill the small target information loss problem. This paper verifies the efficacy of the proposed DDRPNet on the Cityscapes dataset, and the proposed model reaches 76.28% average intersection and merger ratio with 46.3 FPS speed. On the CamVid dataset, the proposed model reaches 73.2% average intersection and merger ratio with 95.2 FPS speed. The model achieves a good balance between accuracy and speed, and the semantic segmentation performance is significantly improved, which has potential applications in the field of intelligent driving.

Key words: real-time semantic segmentation, two-branch structured, coordinate attention mechanism, intelligent driving

苗思琦, 杜煜, 严超, 徐成, 孙慧荟. 改进并行双分支结构的实时性语义分割算法研究[J]. 计算机工程与应用, 2025, 61(5): 233-240.

MIAO Siqi, DU Yu, YAN Chao, XU Cheng, SUN Huihui. Study of Real-Time Semantic Segmentation Algorithms with Improved Parallel Two-Branch Structure[J]. Computer Engineering and Applications, 2025, 61(5): 233-240.

参考文献

[1] BENITEZ-GARCIA G, PRUDENTE-TIXTECO L, CASTRO-MADRID L C, et al. Improving real-time hand gesture recognition with semantic segmentation[J]. Sensors, 2021, 21(2): 356.
[2] 鹿鑫, 杜煜, 陈泽宇, 等. 基于改进BiSeNet的语义分割算法[J]. 传感器与微系统, 2023, 42(7): 136-139.
LU X, DU Y, CHEN Z Y, et al. Semantic segmentation algorithm based on improved BiSeNet[J]. Transducer and Microsystem Technologies, 2023, 42(7): 136-139.
[3] RONNEBERGER O, FISCHER P, BROX T. U-Net: convolutional networks for biomedical image segmentation[C]//Proceedings of the 18th International Conference on Medical Image Computing and Computer-Assisted Intervention, 2015: 234-241.
[4] SAHA M, CHAKRABORTY C. Her2Net: a deep framework for semantic segmentation and classification of cell membranes and nuclei in breast cancer evaluation[J]. IEEE Transactions on Image Processing, 2018, 27(5): 2189-2200.
[5] 苏健民, 杨岚心, 景维鹏. 基于U-Net的高分辨率遥感图像语义分割方法[J]. 计算机工程与应用, 2019, 55(7): 207-213.
SU J M, YANG L X, JING W P. U-Net based semantic segmentation method for high resolution remote sensing image[J]. Computer Engineering and Applications, 2019, 55(7): 207-213.
[6] CORTES C, VAPNIK V. Support-vector networks[J]. Machine Learning, 1995, 20: 273-297.
[7] HO T K. Random decision forests[C]//Proceedings of the 3rd International Conference on Document Analysis and Recognition, 1995: 278-282.
[8] LONG J, SHELHAMER E, DARRELL T. Fully convolutional networks for semantic segmentation[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2015: 3431-3440.
[9] IANDOLA F N, HAN S, MOSKEWICZ M W, et al. SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and<0.5 MB model size[J]. arXiv:1602.07360, 2016.
[10] SIMONYAN K, ZISSERMAN A. Very deep convolutional networks for large-scale image recognition[J]. arXiv:1409.1556,2014.
[11] AL-QIZWINI M, BARJASTEH I, AL-QASSAB H, et al. Deep learning algorithm for autonomous driving using googlenet[C]//Proceedings of the 2017 IEEE Intelligent Vehicles Symposium , 2017: 89-96.
[12] CHEN L C, PAPANDREOU G, KOKKINOS I, et al. DeepLAB: semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected CRFS[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2017, 40(4): 834-848.
[13] LIN G, MILAN A, SHEN C, et al. RefineNet: multi-path refinement networks for high-resolution semantic segmentation[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2017: 1925-1934.
[14] CHEN L C, ZHU Y, PAPANDREOU G, et al. Encoder-decoder with atrous separable convolution for semantic image segmentation[C]//Proceedings of the European Conference on Computer Vision, 2018: 801-818.
[15] ZHAO H, SHI J, QI X, et al. Pyramid scene parsing network[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2017: 2881-2890.
[16] LI H C, XIONG P F, FAN H Q, et al. DFANet: deep feature aggregation for real-time semantic segmentation[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2019: 9522-9531.
[17] YU C, WANG J, PENG C, et al. BiSeNet: bilateral segmentation network for real-time semantic segmentation[C]//Proceedings of the European Conference on Computer Vision, 2018: 325-341.
[18] PAN H, HONG Y, SUN W, et al. Deep dual-resolution networks for real-time and accurate semantic segmentation of traffic scenes[J]. IEEE Transactions on Intelligent Transportation Systems, 2022, 24(3): 3448-3460.
[19] BADRINARAYANAN V, KENDALL A, CIPOLLA R. SegNet: a deep convolutional encoder-decoder architecture for image segmentation[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2017, 39(12): 2481-2495.
[20] LIU J B, HE J J, ZHANG J W, et al. EfficientFCN: holistically-guided decoding for semantic segmentation[C]//Proceedings of the European Conference on Computer Vision, 2020: 1-17.
[21] TAKAHASHI N, MITSUFUJI Y. Densely connected multi-dilated convolutional networks for dense prediction tasks[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2021: 993-1002.
[22] MEHTA S, RASTEGARI M, CASPI A, et al. ESPNet: efficient spatial pyramid of dilated convolutions for semantic segmentation[C]//Proceedings of the European Conference on Computer Vision, 2018: 552-568.
[23] ZHANG H, ZU K, LU J, et al. EPSANet: an efficient pyramid squeeze attention block on convolutional neural network[C]//Proceedings of the Asian Conference on Computer Vision, 2022: 1161-1177.
[24] HU J, SHEN L, SUN G. Squeeze-and-excitation networks[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2018: 7132-7141.
[25] WOO S, PARK J, LEE J Y, et al. CBAM: convolutional block attention module[C]//Proceedings of the European Conference on Computer Vision, 2018: 3-19.
[26] CORDTS M, OMRAN M, RAMOS S, et al. The cityscapes dataset for semantic urban scene understanding[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2016: 3213-3223.
[27] BROSTOW G J, FAUQUEUR J, CIPOLLA R. Semantic object classes in video: a high-definition ground truth database[J]. Pattern Recognition Letters, 2009, 30(2): 88-97.
[28] ZHAO H, QI X, SHEN X, et al. ICNet for real-time semantic segmentation on high-resolution images[C]//Proceedings of the European Conference on Computer Vision, 2018: 405-420.
[29] YU C, GAO C, WANG J, et al. BiSeNet v2: bilateral network with guided aggregation for real-time semantic segmentation[J]. International Journal of Computer Vision, 2021, 129: 3051-3068.
[30] FAN M, LAI S, HUANG J, et al. Rethinking BiSeNet for real-time semantic segmentation[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2021: 9716-9725.
[31] WANG H, JIANG X, REN H, et al. SwiftNet: real-time video object segmentation[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2021: 1296-1305.
[32] KUMAAR S, LYU Y, NEX F, et al. CABiNet: efficient context aggregation network for low-latency semantic segmentation[C]//Proceedings of the 2021 IEEE International Conference on Robotics and Automation, 2021: 13517-13524.
[33] PASZKE A, CHAURASIA A, KIM S, et al. ENet: a deep neural network architecture for real-time semantic segmentation[J]. arXiv:1606.02147, 2016.
[34] LO S Y, HANG H M, CHAN S W, et al. Efficient dense modules of asymmetric convolution for real-time semantic segmentation[C]//Proceedings of the ACM Multimedia Asia, 2019: 1-6.

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