基于多阶段提议稀疏区域卷积网络的城市交通目标检测

柳长源; 张玉亮; 毕晓君

doi:10.12263/DZXB.20211648

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基于多阶段提议稀疏区域卷积网络的城市交通目标检测

学术论文 | 更新时间：2025-07-02

- 基于多阶段提议稀疏区域卷积网络的城市交通目标检测
- Urban Traffic Object Detection Based on Multi-Stage Proposal Sparse R-CNN
- 电子学报 2023年51卷第1期页码：26-31
- 作者机构：
  
  1.哈尔滨理工大学测控技术与通信工程学院，黑龙江哈尔滨 150080
  2.中央民族大学信息工程学院，北京 100081
- 作者简介：
  
  [ "柳长源　男.1970年10月出生，黑龙江肇东人.副教授、硕导.1993年、2005年、2013年分别在吉林大学、哈尔滨理工大学、哈尔滨工程大学获理学学士、工学硕士和工学博士学位，现为哈尔滨理工大学测控技术与通信工程学院教师，主要从事模式识别、机器学习、图像处理等方面的研究工作.E-mail： liuchangyuan@hrbust.edu.cn" ]
  [ "张玉亮　男.1998年1月出生于安徽省阜阳市.哈尔滨理工大学测控技术与通信工程学院硕士研究生.研究方向为模式识别、目标检测.E-mail： 2497484650@qq.com" ]
  [ "毕晓君　女.1964年11月生于黑龙江哈尔滨.教授、博士生导师.1987年、 1990年、2006年于哈尔滨工程大学、哈尔滨工业大学、哈尔滨工程大学获工学学士、工学硕士和工学博士学位，现为中央民族大学信息工程学院教师，主要研究进化计算、数据挖掘." ]
- 基金信息：
  
  国家自然科学面上基金(51779050);黑龙江省自然科学基金(F2016022)
- DOI：10.12263/DZXB.20211648
  中图分类号： TP391.4;TP181
- 收稿：2021-12-12，
  
  修回：2022-07-01，
  
  纸质出版：2023-01-25
- 稿件说明：
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柳长源,张玉亮,毕晓君.基于多阶段提议稀疏区域卷积网络的城市交通目标检测[J].电子学报,2023,51(01):26-31.

LIU Chang-yuan,ZHANG Yu-liang,BI Xiao-jun.Urban Traffic Object Detection Based on Multi-Stage Proposal Sparse R-CNN[J].ACTA ELECTRONICA SINICA,2023,51(01):26-31.
柳长源,张玉亮,毕晓君.基于多阶段提议稀疏区域卷积网络的城市交通目标检测[J].电子学报,2023,51(01):26-31. DOI： 10.12263/DZXB.20211648.

LIU Chang-yuan,ZHANG Yu-liang,BI Xiao-jun.Urban Traffic Object Detection Based on Multi-Stage Proposal Sparse R-CNN[J].ACTA ELECTRONICA SINICA,2023,51(01):26-31. DOI： 10.12263/DZXB.20211648.

摘要

针对城市交通场景多目标检测算法检测速度慢，检测精度低等问题，本文提出多阶段提议稀疏区域卷积网络算法（Multi-stage Proposal Sparse Region-based Convolutional Neural Network，MPS R-CNN）.算法主要有以下特点：提出了一种多阶段提议框过滤更新机制，提高算法检测精度；提出了一种双向并联特征金字塔网络（Bidirectional Parallel Feature Pyramid Network，BPFPN），增强了模型的特征融合能力；针对城市交通场景目标检测问题引入了Copy-Paste数据增强方法和CIoU损失函数.实验结果显示，MPS R-CNN算法在Urban Object Dataset数据集上mAP达到了77%，算法检测速度保持在37fps，优于目前其他城市交通场景目标检测算法.

Abstract

Aiming at the slow speed and low accuracy of multi-object detection algorithms in urban traffic scenes

this paper proposes a multi-stage proposal sparse region-based convolutional neural network algorithm (MPS R-CNN). The algorithm mainly has the following characteristics: a multi-stage proposal box filtering update mechanism is proposed to improve the detection accuracy of the algorithm; a bidirectional parallel feature pyramid network (BPFPN) is proposed to enhance the model feature fusion capability; for the problem of object detection in urban traffic scenes

the Copy-Paste data augmentation method and CIoU loss function are introduced. The experimental results show that the MPS R-CNN algorithm achieves 77% mAP on the urban object dataset

and the algorithm detection speed remains at 37 fps

which is better than other current urban traffic object detection algorithms.

关键词

Keywords

references

LOWE D G . Distinctive image features from scale-invariant keypoints [J]. International Journal of Computer Vision , 2004 , 60 ( 2 ): 91 - 110 .

GIRSHICK R , DONAHUE J , DARRELL T , et al . Rich feature hierarchies for accurate object detection and semantic segmentation [C]// Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition . Piscataway : IEEE , 2014 : 580 - 587 .

REDMON J , FARHADI A . YOLO9000: Better, faster, stronger [C]// Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition . Piscataway : IEEE , 2017 : 7263 - 7271 .

李宝奇 , 贺昱曜 , 强伟 , 何灵蛟 . 基于并行附加特征提取网络的SSD地面小目标检测模型 [J]. 电子学报 , 2020 , 48 ( 1 ): 84 - 91 .

LI Baoqi , HE Yuyao , QIANG Wei , HE Lingjiao . SSD small target detection model based on parallel additional feature extraction network [J]. Acta Electronica Sinica , 2020 , 48 ( 1 ): 84 - 91 . (in Chinese)

HE K , GKIOXARI G , DOLLÁR P , et al . Mask r-cnn [C]// Proceedings of the IEEE International Conference on Computer Vision . Piscataway : IEEE , 2017 : 2961 - 2969 .

LIN T Y , GOYAL P , GIRSHICK R , et al . Focal loss for dense object detection [C]// Proceedings of the IEEE International Conference on Computer Vision . Piscataway : IEEE , 2017 : 2980 - 2988 .

SUN P , ZHANG R , JIANG Y , et al . Sparse r-cnn: End-to-end object detection with learnable proposals [C]// Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition . Piscataway : IEEE , 2021 : 14454 - 14463 .

HE K , ZHANG X , REN S , et al . Deep residual learning for image recognition [C]// Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition . Piscataway : IEEE , 2016 : 770 - 778 .

LIN T Y , DOLLÁR P , GIRSHICK R , et al . Feature pyramid networks for object detection [C]// Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition . Piscataway : IEEE , 2017 : 2117 - 2125 .

侯庆山 , 邢进生 . 基于Grad-CAM与KL损失的SSD目标检测算法 [J]. 电子学报 , 2020 , 48 ( 12 ): 2409 - 2416 .

HOU Qingshan , XING Jinsheng . SSD target detection algorithm based on Grad-CAM and KL loss [J]. Acta Electronica Sinica , 2020 , 48 ( 12 ): 2409 - 2416 . (in Chinese)

GHIASI G , CUI Y , SRINIVAS A , et al . Simple copy-paste is a strong data augmentation method for instance segmentation [C]// Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition . Piscataway : IEEE , 2021 : 2918 - 2928 .

ZHENG Z , WANG P , REN D , et al . Enhancing geometric factors in model learning and inference for object detection and instance segmentation [J]. IEEE Transactions on Cybernetics , 2021 , 62 ( 01 ): 1 - 13 .

LIU S , QI L , QIN H , et al . Path aggregation network for instance segmentation [C]// Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition . Piscataway : IEEE , 2018 : 8759 - 8768 .

DOMINGUEZ-SANCHEZ A , CAZORLA M , ORTS-ESCOLANO S . A new dataset and performance evaluation of a region-based cnn for urban object detection [J]. Electronics , 2018 , 7 ( 11 ): 301 .

GLENN J . ultralytics/yolov5 [EB/OL]. ( 2021-07-08 )[ 2021-07-08 ]. https://github.com/ultralytics/yolov5 https://github.com/ultralytics/yolov5 .

TAN M , PANG R , LE Q V . Efficientdet: Scalable and efficient object detection [C]// Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition . Piscataway : IEEE , 2020 : 10781 - 10790 .

PSALTIS A , DIMOU A , ALVAREZ F , et al . Flow R-CNN: Flow-enhanced object detection [C]// ICPR International Workshops and Challenges . Berlin : Springer , 2021 : 685 - 700 .

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