基于函数改进的YOLOv3车辆检测与识别算法

宋华杰; 周磊

doi:10.11959/j.issn.2096-6652.202301

您当前的位置：

首页 >

文章列表页 >

基于函数改进的YOLOv3车辆检测与识别算法

学术论文 | 更新时间：2024-04-07

- 基于函数改进的YOLOv3车辆检测与识别算法
- Vehicle detection and recognition algorithm based on function improvement of YOLOv3
- 智能科学与技术学报 2023年5卷第4期页码：535-542
- 作者机构：
  
  1.西安铁路职业技术学院，陕西西安 710026
  2.西安工程大学，陕西西安 710048
- 作者简介：
  
  [ "宋华杰（1990- ），女，西安铁路职业技术学院助教，主要研究方向为城轨车辆与车辆信息识别。" ]
  [ "周磊（1981- ），男，西安铁路职业技术学院助教，主要研究方向为城轨车辆检修与驾驶。" ]
- 基金信息：
  
  西安铁路职业技术学院2023年度立项课题(XTZY23K12)
- DOI：10.11959/j.issn.2096-6652.202301
  中图分类号： TP14
- 收稿：2021-11-01，
  
  修回：2021-12-23，
  
  纸质出版：2023-12-15
- 稿件说明：
移动端阅览
宋华杰, 周磊. 基于函数改进的YOLOv3车辆检测与识别算法[J]. 智能科学与技术学报, 2023,5(4):535-542.

SONG Huajie, ZHOU Lei. Vehicle detection and recognition algorithm based on function improvement of YOLOv3[J]. Chinese Journal of Intelligent Science and Technology, 2023, 5(4): 535-542.
宋华杰, 周磊. 基于函数改进的YOLOv3车辆检测与识别算法[J]. 智能科学与技术学报, 2023,5(4):535-542. DOI： 10.11959/j.issn.2096-6652.202301.

SONG Huajie, ZHOU Lei. Vehicle detection and recognition algorithm based on function improvement of YOLOv3[J]. Chinese Journal of Intelligent Science and Technology, 2023, 5(4): 535-542. DOI： 10.11959/j.issn.2096-6652.202301.

摘要

YOLOv3算法在目标检测和识别方面具有检测精度高、速度快等优点，但其损失函数中高宽比部分的损失运算对大目标与小目标的区分不明显，导致损失计算结果精度较差，小目标预测框尺寸不准确。为此，对YOLOv3算法的损失函数进行了改进，将宽高坐标误差部分修改为与目标宽高真实值相关的形式，并将原模型的非极大值抑制函数中有重叠区域的检测框的分数由固定值改为衰减函数的形式，最后对改进模型进行了车辆检测实验。研究表明，改进模型在不影响车辆检测速度的同时，召回率

、精确率

、调和F1值和平均准确度mAP分别提高了0.1、0.105、0.103、0.021，改进模型的整体性能优于原YOLOv3模型。

Abstract

YOLOv3 algorithm is characterized by high detection accuracy and high speed in the aspect of target detection and recognition. It is outstanding among similar algorithms

but there are still obvious problems. The loss operation of the wide and high part of the loss function of this algorithm is not obvious to the distinction between the big target and the small target

which leads to the problem that the loss calculation is not accurate enough and the prediction box size of the small target is not accurate. In view of this defect

the loss function of YOLOv3 algorithm was improved

the wide-height coordinate error was modified into the form of proportion

the non-maximum suppression method of the original model was improved and the overlap threshold was changed from the fixed value to the form of attenuation function. Finally

the model was applied to vehicle detection. The experimental results showed that the improved model improved the accuracy rate

recall rate and F1 value to different degrees while basically not affecting the vehicle detection speed

making the overall performance of the model better than the original YOLOv3 model.

关键词

Keywords

references

刘凯 , 李浥东 , 林伟鹏 . 车辆再识别技术综述 [J ] . 智能科学与技术学报 , 2020 , 2 ( 1 ): 10 - 25 .

LIU K , LI Y D , LIN W P . A survey on vehicle re-identification [J ] . Chinese Journal of Intelligent Science and Technology , 2020 , 2 ( 1 ): 10 - 25 .

SEVILLA-LARA L , SUN D Q , JAMPANI V , et al . Optical flow with semantic segmentation and localized layers [C ] // Proceedings of 2016 IEEE Conference on Computer Vision and Pattern Recognition . Piscataway : IEEE Press , 2016 : 3889 - 3898 .

于晓明 , 李思颖 , 史胜楠 . 混合高斯融合三帧差的运动目标检测改进算法 [J ] . 红外技术 , 2019 , 41 ( 3 ): 256 - 261 .

YU X M , LI S Y , SHI S N . An improved algorithm for moving target detection using a Gaussian mixture with three-frame difference [J ] . Infrared Technology , 2019 , 41 ( 3 ): 256 - 261 .

SONG Z Y , ALI S , BOUGUILA N . Background subtraction using infinite asymmetric Gaussian mixture models with simultaneous feature selection [J ] . IET Image Processing , 2020 , 14 ( 11 ): 2321 - 2332 .

LUO J H , LIN C H . Pure FPGA implementation of an HOG based real-time pedestrian detection system [J ] . Sensors(Basel) , 2018 , 18 ( 4 ): 1174 .

曾娟 , 李守义 , 张洪昌 . 图像分区域多特征融合斜向车辆检测算法研究 [J ] . 公路交通科技 , 2020 , 37 ( 8 ): 99 - 107 .

ZENG J , LI S Y , ZHANG H C . Study on sub-region multi-feature image fusion oblique vehicle detection algorithm [J ] . Journal of Highway and Transportation Research and Development , 2020 , 37 ( 8 ): 99 - 107 .

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

GIRSHICK R . Fast R-CNN [C ] // Proceedings of 2015 IEEE International Conference on Computer Vision . Piscataway : IEEE Press , 2016 : 1440 - 1448 .

REN S Q , HE K M , GIRSHICK R , et al . Faster R-CNN: towards real-time object detection with region proposal networks [C ] // Proceedings of IEEE Transactions on Pattern Analysis and Machine Intelligence . Piscataway : IEEE Press , 2016 : 1137 - 1149 .

HE K M , ZHANG X Y , REN S Q , et al . Spatial pyramid pooling in deep convolutional networks for visual recognition [J ] . IEEE Transactions on Pattern Analysis and Machine Intelligence , 2015 , 37 ( 9 ): 1904 - 1916 .

REDMON J , DIVVALA S , GIRSHICK R , et al . You only look once: unified, real-time object detection [C ] // Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition , 2016 : 779 - 788 .

田思佳 , 顾强 , 胡蓉 , 等 . 一种基于深度学习的机械臂分拣方法 [J ] . 智能科学与技术学报 , 2020 , 2 ( 3 ): 268 - 274 .

TIAN S J , GU Q , HU R , et al . A robot sorting method based on deep learning [J ] . Chinese Journal of Intelligent Science and Technology , 2020 , 2 ( 3 ): 268 - 274 .

刘肯 , 何姣姣 , 张永平 , 等 . 改进YOLO的车辆检测算法 [J ] . 现代电子技术 , 2019 , 42 ( 13 ): 47 - 50 .

LIU K , HE J J , ZHANG Y P , et al . Improved YOLO vehicle detection algorithm [J ] . Modern Electronics Technique , 2019 , 42 ( 13 ): 47 - 50 .

REDMON J , FARHADI A . YOLO9000: better, faster, stronger [C ] // Proceedings of 2017 IEEE Conference on Computer Vision and Pattern Recognition . Piscataway : IEEE Press , 2017 : 6517 - 6525 .

REDMON J, FARHADI A. YOLOv3: an incremental improvement[J].arXiv preprint, 2018, arXiv: 1804.02767.

阮激扬 . 基于YOLO的目标检测算法设计与实现 [D ] . 北京 : 北京邮电大学 , 2019 .

RUAN J Y . Design and implementation of object detection algorithm based on YOLO [D ] . Beijing : Beijing University of Posts and Telecommunications , 2019 .

杨眷玉 . 基于卷积神经网络的物体识别研究与实现 [D ] . 成都 : 电子科技大学 , 2016 .

YANG J Y . Research and implementation of object detection based on convolutional neural networks [D ] . Chengdu : University of Electronic Science and Technology of China , 2016 .

浏览量

480

下载量

CSCD

文章被引用时，请邮件提醒。

提交

工具集

关联资源

Cerberus：基于深度学习的跨站社交网络机器人检测系统