Journal of Jilin University(Engineering and Technology Edition) ›› 2025, Vol. 55 ›› Issue (9): 3032-3041.doi: 10.13229/j.cnki.jdxbgxb.20250454

Previous Articles    

Edge feature⁃guided semantic segmentation method for intelligent vehicle

Zhen HUO1(),Li-sheng JIN1,2(),Qiang HUA3, HEYang1   

  1. 1.School of Vehicle and Energy，Yanshan University，Qinhuangdao 066004，China
    2.Hebei Key Laboratory of Special Carrier Equipment，Yanshan University，Qinhuangdao 066004，China
    3.BYD Auto Industry Company Limited，Shenzhen 518118，China
  • Received:2025-05-24 Online:2025-09-01 Published:2025-11-14
  • Contact: Li-sheng JIN E-mail:vehicle_huo@stumail.ysu.edu.cn;jinls@ysu.edu.cn

RICH HTML

 0   

PDF (PC)

41

Abstract:

Regarding the issue that existing semantic segmentation algorithms fail to fully exploit target edge features in autonomous driving scenarios， an edge feature-guided semantic segmentation method for intelligent vehicles was proposed. First， an edge feature enhancement module was introduced to extract edge information from the original image as a parallel input to the encoder， strengthening the neural network's edge feature representation. Second， an edge attention module was proposed to fuse original image features and edge features， adaptively balancing their contributions in different regions for semantic segmentation. Finally， an efficient channel pyramid pooling module was designed in the decoder to improve the utilization efficiency of contextual semantic features after pooling at different scales. Experimental results on the Cityscapes dataset demonstrate that the proposed method achieves 76.1% MIoU with a processing speed of 132.6 f/s， outperforming the baseline PP-LiteSeg by 2.2% MIoU while maintaining real-time performance， balancing segmentation accuracy and computational efficiency.

Key words: intelligent vehicle, semantic segmentation, edge feature enhancement, edge attention mechanism, efficient channel pyramid pooling

CLC Number: 

  • U491.2

Fig.1

Edge feature-guided semantic segmentation network framework"

Fig.2

Example of edge feature enhancement"

Fig.3

Edge attention module"

Fig.4

Efficient channel pyramid pooling module"

Table 1

Comparison of semantic segmentation performance for different methods"

方法年份输入图像尺寸骨干网络MIOU/%速度/（f·s-1）
Enet［11］2016512×1 024—58.376.9
BiSeNetV1［12］2018768×1 536Xception3968.4105.8
BiSeNetV1［12］2018768×1 536ResNet1874.765.5
GSCNN［15］20191 024×2 048—80.8—
STDC1［18］2021512×1 024STDC171.9250.4
STDC2［18］2021512×1 024STDC273.4188.6
BiSeNetV2［25］2021512×1 024—72.6156
BiSeNetV2-L［25］2021512×1 024—75.347.3
Edgenet［26］2020512×1 024—71.031.4
PP-LiteSeg-T［13］2022512×1 024STDC172.0273.6
PP-LiteSeg-B［13］2022512×1 024STDC273.9195.3
BiSeNetV3［27］2023512×1 024STDC173.5244.3
BiSeNetV3［27］2023512×1 024STDC274.5180.3
ALANet［28］2024512×1 024—74.4115.6
本文（STDC1）—512×1 024STDC174.6177.8
本文（STDC2）—512×1 024STDC276.1132.6

Fig.5

Visualization of semantic segmentation results for different methods"

Table 2

Ablation study comparison of semantic segmentation performance"

基线算法EFEMEAMECPPMMIOU/%速度/（f·s-1）F-score（3 px）F-score（5 px）F-score（9 px）F-score（12 px）
√———73.9195.367.871.574.475.2
√√——74.4152.669.573.976.377.0
√√√—75.0139.371.875.978.580.1
√√√√76.1132.672.576.779.280.5

Fig.6

Visualization of semantic segmentation results for ablation study"

Fig.7

Visualization of semantic segmentation results of real road scenes"

[1] 闵莉, 董冰洁, 安冬. 基于多注意力机制与跨特征融合的语义分割算法[J]. 计算机工程, 2024, 50(8): 282-289.
Min Li, Dong Bing-jie, An Dong. Semantic segmentation algorithm based on multi-attention mechanism and cross-feature fusion[J]. Computer Engineering, 2024, 50(8): 282-289.
[2] Liang Y, Xu Z, Rasti S, et al. On the use of a semantic segmentation micro-service in AR devices for UI placement[C]∥2022 IEEE Games, Entertainment, Media Conference, Bridgetown, Barbados, 2022: 1-6.
[3] Yang L, Bai Y, Ren F, et al. Lcfnets: compensation strategy for real-time semantic segmentation of autonomous driving[J]. IEEE Transactions on Intelligent Vehicles, 2024, 9(4): 4715-4729.
[4] Zhou Z, Rahman Siddiquee M M, Tajbakhsh N, et al. Unet++: a nested U-net architecture for medical image segmentation[C]∥Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support, Granada, Spain, 2018: 3-11.
[5] Mo Y, Wu Y, Yang X, et al. Review the state-of-the-art technologies of semantic segmentation based on deep learning[J]. Neurocomputing, 2022, 493: 626-646.
[6] Muhammad K, Hussain T, Ullah H, et al. Vision-based semantic segmentation in scene understanding for autonomous driving: recent achievements, challenges, and outlooks[J]. IEEE Transactions on Intelligent Transportation Systems, 2022, 23(12): 22694-22715.
[7] Shelhamer E, Long J, Darrell T. Fully convolutional networks for semantic segmentation[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2016, 39(4): 640-651.
[8] Ronneberger O, Fischer P, Brox T. U-net: convolutional networks for biomedical image segmentation[C]∥Medical Image Computing and Computer-Assisted Intervention, Munich, Germany, 2015: 234-241.
[9] 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.
[10] Zhao H S, Shi J P, Qi X J, et al. Pyramid scene parsing network[C]∥Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition,Honolulu, USA, 2017: 6230-6239,.
[11] Paszke A, Chaurasia A, Kim S, et al. Enet: a deep neural network architecture for real-time semantic segmentation[J/OL].[2025-05-10].
[12] 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 (ECCV),Munich, Germany, 2018: 334-349.
[13] Peng J, Liu Y, Tang S, et al. Pp-liteseg: a superior real-time semantic segmentation model[J/OL]. [2025-05-10].
[14] Xu J, Xiong Z, Bhattacharyya S P. PIDNet: a real-time semantic segmentation network inspired by PID controllers[C]∥Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, Vancouver, Canada,2023: 19529-19539.
[15] Takikawa T, Acuna D, Jampani V, et al. Gated-SCNN: Gated shape cnns for semantic segmentation[C]∥Proceedings of the IEEE/CVF International Conference on Computer Vision, Seoul, Korea, 2019: 5228-5237.
[16] Yu L, Yao A, Duan J. Improving semantic segmentation via decoupled body and edge information[J]. Entropy, 2023, 25(6): No. 891.
[17] Ding L, Goshtasby A. On the Canny edge detector[J]. Pattern Recognition, 2001, 34(3): 721-725.
[18] 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,Nashville, USA, 2021: 9711-9720.
[19] Niu Z, Zhong G, Yu H. A review on the attention mechanism of deep learning[J]. Neurocomputing, 2021, 452: 48-62.
[20] Wang Q, Wu B, Zhu P, et al. ECA-Net: efficient channel attention for deep convolutional neural networks[C]∥Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, Seattle, USA, 2020: 11531-11539.
[21] 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,Las Vegas, USA, 2016: 3213-3223.
[22] Zhou Q, Wang L, Gao G, et al. Boundary-guided lightweight semantic segmentation with multi-scale semantic context[J]. IEEE Transactions on Multimedia, 2024, 26: 7887-7900.
[23] 江晟,路启,夏淼磊,等.基于特征融合注意力的RGB-D语义分割算法[J/OL]. [2025-05-10].
[24] Perazzi F, Pont-Tuset J, McWilliams B, et al. A benchmark dataset and evaluation methodology for video object segmentation[C]∥Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, USA, 2016: 724-732.
[25] 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.
[26] Han H Y, Chen Y C, Hsiao P Y, et al. Using channel-wise attention for deep CNN based real-time semantic segmentation with class-aware edge information[J]. IEEE Transactions on Intelligent Transportation Systems, 2020, 22(2): 1041-1051.
[27] Tsai T H, Tseng Y W. BiSeNet V3: bilateral segmentation network with coordinate attention for real-time semantic segmentation[J]. Neurocomputing, 2023, 532: 33-42.
[28] Liu Q, Wang C, Li Z, et al. Attention based lightweight asymmetric network for real-time semantic segmentation[J]. Engineering Applications of Artificial Intelligence, 2024, 130: No.107736.
[1] Jun-wu ZHAO,Ting QU,Yun-feng HU. Trajectory planning for intelligent vehicles based on adaptive sampling [J]. Journal of Jilin University(Engineering and Technology Edition), 2025, 55(8): 2802-2816.
[2] Ying YU,Chun-ping WANG,Ren-ke KOU,Bo-xiong YANG,Lei WANG,Fu-jun ZHAO,Qiang FU. Semantic segmentation algorithm for multi temporal high⁃resolution satellite remote sensing images [J]. Journal of Jilin University(Engineering and Technology Edition), 2025, 55(6): 2131-2137.
[3] Hua CAI,Yu-yao WANG,Qiang FU,Zhi-yong MA,Wei-gang WANG,Chen-jie ZHANG. Semantic segmentation network based on attention mechanism and feature fusion [J]. Journal of Jilin University(Engineering and Technology Edition), 2025, 55(4): 1384-1395.
[4] Hong-yan GUO,Jia-ming ZHANG,Jun LIU,Yun-feng HU. Construction of virtual test scenario for intelligent vehicle and pedestrian interaction [J]. Journal of Jilin University(Engineering and Technology Edition), 2024, 54(9): 2511-2519.
[5] Xian-yi XIE,Ming-jun ZHANG,Li-sheng JIN,Bin ZHOU,Tao HU,Yu-fei BAI. Artificial bee colony trajectory planning algorithm for intelligent vehicles considering comfortable [J]. Journal of Jilin University(Engineering and Technology Edition), 2024, 54(6): 1570-1581.
[6] Li-xin YAN,Jin-pei FENG,Jun-hua GUO,Yi-ke GONG. Analysis of characteristics of the takeover behavior of co⁃driving intelligent vehicles under different dangerous scenarios [J]. Journal of Jilin University(Engineering and Technology Edition), 2024, 54(3): 683-691.
[7] Xian-yi XIE,Yu-han WANG,Li-sheng JIN,Xin ZHAO,Bai-cang GUO,Ya-ping LIAO,Bin ZHOU,Ke-qiang LI. Intelligent vehicle trajectory tracking control based on adjusting step size of control horizon [J]. Journal of Jilin University(Engineering and Technology Edition), 2024, 54(3): 620-630.
[8] Zhen-hai GAO,Rong-gui CAI,Tian-jun SUN,Tong YU,Hao-yuan ZHAO,Hao BAN. Data⁃filtering method for driving behavior based on vehicle shared autonomy [J]. Journal of Jilin University(Engineering and Technology Edition), 2024, 54(3): 589-599.
[9] Guo LIU,Jian XIONG,Xiu-jian YANG,Yang-fan HE. Intelligent vehicle trajectory tracking control based on curvature augmentation [J]. Journal of Jilin University(Engineering and Technology Edition), 2024, 54(12): 3717-3728.
[10] Yun QUE,Xue JI,Zi-ping JIANG,Yi DAI,Ye-fei WANG,Jia CHEN. Semantic segmentation algorithm of pavement cracks based on GAN data augmentation [J]. Journal of Jilin University(Engineering and Technology Edition), 2023, 53(11): 3166-3175.
[11] Wei ZHANG,Shu-pei ZHANG,Chong-en LUO,Sheng ZHANG,Guo-lin WANG. Collision avoidance trajectory planning for intelligent vehicles in emergency conditions [J]. Journal of Jilin University(Engineering and Technology Edition), 2022, 52(7): 1515-1523.
[12] Xue-yun CHEN,Xue-yu BEI,Qu YAO,Xin JIN. Pedestrian segmentation and detection in multi-scene based on G-UNet [J]. Journal of Jilin University(Engineering and Technology Edition), 2022, 52(4): 925-933.
[13] ZHANG Lin, ZHANG Xin-jie, GUO Kong-hui, WANG Chao, LIU Yang, LIU Tao. Rolling window optimization for intelligent vehicle trajectory planning in unknown environment [J]. 吉林大学学报(工学版), 2018, 48(3): 652-660.
[14] LI Ai-juan,LI Shun-ming,ZHAO Wan-zhong,SHEN Huan,JIANG Xing-xing,
QIU Xu-yun,WANG Hui-jun. Optimal control theory based trajectory generation method for intelligent vehicle [J]. 吉林大学学报(工学版), 2014, 44(5): 1276-1282.
[15] LI Ai-juan, LI Shun-ming, SHEN Huan, MIAO Xiao-dong. ACT-R based dynamic trajectory optimization method for intelligent vehicles [J]. 吉林大学学报(工学版), 2013, 43(05): 1184-1189.
Viewed
Full text


Abstract

Cited
  Shared   
  Discussed   
No Suggested Reading articles found!
Copyright © Journal of Jilin University(Engineering and Technology Edition), All Rights Reserved.
Tel: +86-431-85095297　Fax: +86-431-85094074　E-mail: xbgxb@jlu.edu.cn
Powered by Beijing Magtech Co. Ltd