Vehicle detection and recognition algorithm based on function improvement of YOLOv3

SONG Huajie; ZHOU Lei

doi:10.11959/j.issn.2096-6652.202301

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Vehicle detection and recognition algorithm based on function improvement of YOLOv3

Papers and Reports | 更新时间：2024-04-07

- Vehicle detection and recognition algorithm based on function improvement of YOLOv3
- Chinese Journal of Intelligent Science and Technology Vol. 5, Issue 4, Pages: 535-542(2023)
- 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：
- 基金信息：
  
  2023 Project Initiation Project of Xi'an Railway Vocational and Technical College(XTZY23K12)
- DOI：10.11959/j.issn.2096-6652.202301
  CLC： TP14
- Received：01 November 2021，
  
  Revised：2021-12-23，
  
  Published：15 December 2023
- 稿件说明：
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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：

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

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