| Citation: | CHEN Y，ZHOU F C，ZHANG J J. Railway panoramic segmentation based on recursive gating enhancement and pyramid prediction[J]. Journal of Beijing University of Aeronautics and Astronautics，2025，51（7）：2229-2239 （in Chinese） doi: 10.13700/j.bh.1001-5965.2023.0492
|
A recursive gated enhancement and pyramid prediction railway panoramic segmentation network is proposed to address the issues of insufficient target feature extraction and blurred edge contour segmentation in high-speed railway scene panoramic segmentation. On the basis of the DETR panoramic segmentation model, firstly, an improved multi-scale cascaded CSP-DarkNet53 feature network is constructed to enhance the ability to extract target features from railway scenes of different scales. Then, to improve the ability to extract and segment edge contour information and acquire richer edge feature information, a recursive gating and class feature augmentation module is proposed. Then, deformable attention is introduced into the coding backbone network to further capture context information and reduce the loss of segmentation details. Finally, by improving the pyramid prediction and pixel category segmentation module, the segmentation output of the railway panoramic view is achieved. The suggested approach enhances the original DETR model’s panorama segmentation quality index PQ by 7.4%, the foreground instance target’s panoramic quality index PQTh by 9.7%, and the background-filled area’s panoramic quality index PQSt by 6.6%, according to experimental data. The proposed method has good performance in panoramic image segmentation in railway scenes, and its subjective evaluation is superior to the comparison method.
| [1] |
陈永, 王镇, 周方春. 空间定位与特征泛化增强的铁路异物跟踪检测[J]. 北京亚洲成人在线一二三四五六区学报, 2025, 51(1): 9-18.
CHEN Y, WANG Z, ZHOU F C. Infrared railway foreign objects tracking based on spatial location and feature generalization enhancement[J]. Journal of Beijing University of Aeronautics and Astronautics, 2025, 51(1): 9-18(in Chinese).
|
| [2] |
LI C, XIE Z Y, QIN Y, et al. A multi-scale image and dynamic candidate region-based automatic detection of foreign targets intruding the railway perimeter[J]. Measurement, 2021, 185: 109853. doi: 10.1016/j.measurement.2021.109853
|
| [3] |
路通, 余祖俊, 郭保青, 等. 网状多尺度与双向通道注意力的铁路场景语义分割[J]. 交通运输系统工程与信息, 2023, 23(2): 233-241.
LU T, YU Z J, GUO B Q, et al. Semantic segmentation of railway scene based on reticulated multi-scale and bidirectional channel attention[J]. Journal of Transportation Systems Engineering and Information Technology, 2023, 23(2): 233-241(in Chinese).
|
| [4] |
KIRILLOV A, HE K, GIRSHICK R, et al. Panoptic segmentation[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. Piscataway: IEEE Press, 2019: 9396-9405.
|
| [5] |
KIRILLOV A, GIRSHICK R, HE K M, et al. Panoptic feature pyramid networks[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. Piscataway: IEEE Press, 2019: 6392-6401.
|
| [6] |
ZHANG W, PANG J, CHEN K, et al. K-Net: towards unified image segmentation[EB/OL]. (2021-10-01)[2023-07-01]. http://arxiv.org/abs/2106.14855?context=cs.AI.
|
| [7] |
CARION N, MASSA F, SYNNAEVE G, et al. End-to-end object detection with Transformers[C]//Proceedings of the 16th European Conference on Computer Vision. Berlin: Springer, 2020: 213-229.
|
| [8] |
CHENG B, SCHWING A G, KIRILLOV A. Per-pixel classifica tion is not all you need for semantic segmentation[EB/OL]. (2021-10-31)[2023-07-01]. http://arxiv.org/abs/2107.06278?context=cs.
|
| [9] |
WANG H Y, ZHU Y K, ADAM H, et al. MaX-DeepLab: end-to-end panoptic segmentation with mask Transformers[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. Piscataway: IEEE Press, 2021: 5459-5470.
|
| [10] |
CHENG B W, COLLINS M D, ZHU Y K, et al. Panoptic-DeepLab: a simple, strong, and fast baseline for bottom-up panoptic segmentation[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. Piscataway: IEEE Press, 2020: 12472-12482.
|
| [11] |
CHENG B W, MISRA I, SCHWING A G, et al. Masked-attention mask Transformer for universal image segmentation[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. Piscataway: IEEE Press, 2022: 1280-1289.
|
| [12] |
YANG T J, COLLINS M D, ZHU Y, et al. DeeperLab: single-shot image parser[C]//Proceedings of theIEEE Conference on Computer Vision and Pattern Recognition. Piscataway: IEEE Press, 2019: 1902-5093.
|
| [13] |
毛琳, 任凤至, 杨大伟, 等. 实例特征深度链式学习全景分割网络[J]. 光学精密工程, 2020, 28(12): 2665. doi: 10.37188/OPE.20202812.2665
MAO L, REN F Z, YANG D W, et al. INFNet: deep instance feature chain learning network for panoptic segmentation[J]. Optics and Precision Engineering, 2020, 28(12): 2665(in Chinese). doi: 10.37188/OPE.20202812.2665
|
| [14] |
宁欣, 田伟娟, 于丽娜, 等. 面向小目标和遮挡目标检测的脑启发CIRA-DETR全推理方法[J]. 计算机学报, 2022, 45(10): 2080-2092. doi: 10.11897/SP.J.1016.2022.02080
NING X, TIAN W J, YU L N, et al. Brain-inspired CIRA-DETR full inference model for small and occluded object detection[J]. Chinese Journal of Computers, 2022, 45(10): 2080-2092(in Chinese). doi: 10.11897/SP.J.1016.2022.02080
|
| [15] |
CHEN Y T, CHEN S F. Localizing plucking points of tea leaves using deep convolutional neural networks[J]. Computers and Electronics in Agriculture, 2020, 171: 105298. doi: 10.1016/j.compag.2020.105298
|
| [16] |
ZHAO Z Q, ZHENG P, XU S T, et al. Object detection with deep learning: a review[J]. IEEE Transactions on Neural Networks and Learning Systems, 2019, 30(11): 3212-3232. doi: 10.1109/TNNLS.2018.2876865
|
| [17] |
HUANG G, LIU Z, VAN DER MAATEN L, et al. Densely connected convolutional networks[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. Piscataway: IEEE Press, 2017: 2261-2269.
|
| [18] |
WANG Z M, WANG J S, YANG K, et al. Semantic segmentation of high-resolution remote sensing images based on a class feature attention mechanism fused with Deeplabv3+[J]. Computers and Geosciences, 2022, 158: 104969. doi: 10.1016/j.cageo.2021.104969
|
| [19] |
RAO Y M, ZHAO W L, TANG Y S, et al. HorNet: efficient high-order spatial interactions with recursive gated convolutions[EB/OL]. (2022-10-11)[2023-07-01]. http://arxiv.org/abs/2207.14284v3.
|
| [20] |
冷冰, 冷敏, 常智敏, 等. 基于Transformer结构的深度学习模型用于外周血白细胞检测[J]. 仪器仪表学报, 2023, 44(5): 113-120.
LENG B, LENG M, CHANG Z M, et al. Deep learning model based on Transformer architecture for peripheral blood leukocyte detection[J]. Chinese Journal of Scientific Instrument, 2023, 44(5): 113-120(in Chinese).
|
| [21] |
ZHU X, SU W, LU L, et al. Deformable DETR: deformable Transformers for end-to-end object detection[EB/OL]. (2021-05-18)[2023-07-01]. http://arxiv.org/abs/2010.04159?context=cs.
|
| [22] |
李飞, 胡坤, 张勇, 等. 基于混合域注意力YOLOv4的输送带纵向撕裂多维度检测[J]. 浙江大学学报(工学版), 2022, 56(11): 2156-2167.
LI F, HU K, ZHANG Y, et al. Multi-dimensional detection of longitudinal tearing of conveyor belt based on YOLOv4 of hybrid domain attention[J]. Journal of Zhejiang University (Engineering Science), 2022, 56(11): 2156-2167(in Chinese).
|
| [23] |
WANG Q L, WU B G, ZHU P F, 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. Piscataway: IEEE Press, 2020: 11531-11539.
|
| [24] |
YING X Y, WANG Y Q, WANG L G, et al. A stereo attention module for stereo image super-resolution[J]. IEEE Signal Processing Letters, 2020, 27: 496-500. doi: 10.1109/LSP.2020.2973813
|
| [25] |
REZATOFIGHI H, TSOI N, GWAK J, et al. Generalized intersection over union: a metric and a loss for bounding box regression[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. Piscataway: IEEE Press, 2019: 658-666.
|
Figures(12) / Tables(3)
Copyright © Journal of Beijing University of Aeronautics and Astronautics
Address: Editorial Department of Journal of Beijing University of Aeronautics and Astronautics, 37 Xueyuan Road, Haidian District, Beijing Post Code: 100191 Email: jbuaa@cqjj8.com
Supported by:
Beijing Renhe Information Technology Co., Ltd.
DownLoad:
