基于优化卷积神经网络的车辆特征识别算法研究

陈暄; 吴吉义

doi:10.11959/j.issn.1000-0801.2023188

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基于优化卷积神经网络的车辆特征识别算法研究

研究与开发 | 更新时间：2024-06-05

- 基于优化卷积神经网络的车辆特征识别算法研究
- Research on vehicle feature recognition algorithm based on optimized convolutional neural network
- 电信科学 2023年39卷第10期页码：101-111
- 作者机构：
  
  1. 浙江工业职业技术学院，浙江绍兴 312000
  2. 浙江省人工智能学会，浙江杭州 310027
  3. 浙江大学智能教育研究中心，浙江杭州 310027
- 作者简介：
  
  [ "陈暄（1979- ），男，浙江工业职业技术学院副教授，主要研究方向为云计算、人工智能" ]
  [ "吴吉义（1980- ），男，博士，浙江大学高级工程师，主要研究方向为服务计算、人工智能" ]
- 基金信息：
  
  国家自然科学基金资助项目;The National Natural Science Foundation of China(61702151);国家自然科学基金资助项目;The National Natural Science Foundation of China(61702320);国家自然科学基金资助项目;The National Natural Science Foundation of China(61772334);国家重点研发计划项目;The National Key Research and Development Program of China(2018YFB1003800);浙江省哲学社会科学规划课题;Zhejiang Provincial Philosophy and Social Sciences Planning Project(23NDJC369YB)
- DOI：10.11959/j.issn.1000-0801.2023188
  中图分类号： TP391.14
- 网络出版日期：2023-10，
  
  纸质出版日期：2023-10-20
- 稿件说明：
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陈暄, 吴吉义. 基于优化卷积神经网络的车辆特征识别算法研究[J]. 电信科学, 2023,39(10):101-111.

Xuan CHEN, Jiyi WU. Research on vehicle feature recognition algorithm based on optimized convolutional neural network[J]. Telecommunications science, 2023, 39(10): 101-111.
陈暄, 吴吉义. 基于优化卷积神经网络的车辆特征识别算法研究[J]. 电信科学, 2023,39(10):101-111. DOI： 10.11959/j.issn.1000-0801.2023188.

Xuan CHEN, Jiyi WU. Research on vehicle feature recognition algorithm based on optimized convolutional neural network[J]. Telecommunications science, 2023, 39(10): 101-111. DOI： 10.11959/j.issn.1000-0801.2023188.

摘要

针对道路场景图像中不同距离目标车辆特征存在识别效果弱、精度低的问题，提出一种基于优化卷积神经网络的车辆特征识别算法。首先，采用基于PAN模型的多尺度输入获取不同距离的目标车辆特征；其次，在卷积神经网络结构中加入多池化、BN层和Leaky ReLU激活函数改进网络模型的性能，通过引入混合注意力机制，集中关注车辆图像中的重要特征和区域，从而增强了网络模型的泛化能力；最后，通过构建多层次卷积神经网络结构完成对车辆的特征效果识别。仿真实验结果表明，在单一场景的BIT-Vehicle数据库中，本文算法相比CNN、R-CNN、ABC-CNN、Faster R-CNN、AlexNet、VGG16和YOLOV8在单一目标和多目标识别率方面分别提高了 16.75%、10.9%、4%、3.7%、2.46%、1.3%、1%和 17.8%、10.5%、2.5%、3.8%、2.7%、1.1%、1.3%，在复杂场景的UA-DETRAC数据库中，本文算法相比其他算法在不同距离目标车辆识别中获得了更加精确的效果。

Abstract

To address the issue of weak identification and low accuracy in recognizing features of target vehicles at different distances in road scene images

a vehicle feature recognition algorithm based on optimized convolutional neural network (CNN) was proposed.Firstly

a multi-scale input based on the PAN model was employed to capture target vehicle features at varying distances.Subsequently

improvements were made to the network model by incorporating multi-pool

batch normalization (BN) layers

and Leaky ReLU activation functions within the CNN architecture.Furthermore

the generalization ability of the network model was enhanced by introducing a hybrid attention mechanism that focuses on important features and regions in the vehicle image.Lastly

a multi-level CNN structure was constructed to achieve feature recognition for vehicles.Simulation experiment results conducted on the BIT-Vehicle database within a single scene show the proposed algorithm’s significant enhancements in single-object and multi-object recognition rates compared to CNN

R-CNN

ABC-CNN

Faster R-CNN

AlexNet

VGG16

and YOLOV8.Specifically

improvements of 16.75%

10.9%

3.7%

2.46%

1.3%

and 1% in single-object recognition

as well as 17.8%

10.5%

2.5%

3.8%

2.7%

1.1%

and 1.3% in multi-object recognition

have been demonstrated by the proposed algorithm

respectively.Over the more complex UA-DETRAC datasets

more precise results have been also achieved by the proposed algorithm in recognizing target vehicles at various distances compared to other algorithms.

关键词

Keywords

references

ZHANG J P , WANG F Y , WANG K F , et al . Data-driven intelligent transportation systems:a survey [J ] . IEEE Transactions on Intelligent Transportation Systems , 2011 , 12 ( 4 ): 1624 - 1639 .

COLLINS R T , LIPTON A J , KANADE T . Introduction to the special section on video surveillance [J ] . IEEE Transactions on Pattern Analysis and Machine Intelligence , 2000 , 22 ( 8 ): 745 - 746 .

CHAROUH Z , EZZOUHRI A , GHOGHO M , et al . A resource-efficient CNN-based method for moving vehicle detection [J ] . Sensors , 2022 , 22 ( 3 ): 1193 .

RANJITHKUMAR S , CHENTHUR PANDIAN S . Automatic license plate recognition system for vehicles using a CNN [J ] . Computers,Materials ＆ Continua , 2022 , 71 ( 1 ): 35 - 50 .

SHI R W , YANG S C , CHEN Y Y , et al . CNN-Transformer for visual-tactile fusion applied in road recognition of autonomous vehicles [J ] . Pattern Recognition Letters , 2023 , 166 : 200 - 208 .

SATYANARAYANA G S R , DESHMUKH P , DAS S K . Vehicle detection and classification with spatio-temporal information obtained from CNN [J ] . Displays , 2022 ,75:102294.

SOON F C , KHAW H Y , CHUAH J H , et al . Hyper-parameters optimisation of deep CNN architecture for vehicle logo recognition [J ] . IET Intelligent Transport Systems , 2018 , 12 ( 8 ): 939 - 946 .

王上 , 唐欢容 . 一种基于混合粒子群优化算法的深度卷积神经网络架构搜索方法 [J ] . 计算机应用研究 , 2023 , 40 ( 7 ): 2019 - 2024 .

WANG S , TANG H R . Deep convolutional neural architecture search method based on hybrid particle swarm optimization algorithm [J ] . Application Research of Computers , 2023 , 40 ( 7 ): 2019 - 2024 .

KIYMAÇ E , KAYA Y . A novel automated CNN arrhythmia classifier with memory-enhanced artificial hummingbird algorithm [J ] . Expert Systems With Applications , 2023 ( 213 ): 119162 .

GHASEMI DAREHNAEI Z , SHOKOUHIFAR M , YAZDANJOUEI H , et al . SI-EDTL:swarm intelligence ensemble deep transfer learning for multiple vehicle detection in UAV images [J ] . Concurrency and Computation:Practice and Experience , 2022 , 34 ( 5 ): e6726 .

MAITY M , BANERJEE S , SINHA CHAUDHURI S . Faster R-CNN and YOLO based vehicle detection:a survey [C ] // Proceedings of 2021 5th International Conference on Computing Methodologies and Communication (ICCMC) . Piscataway:IEEE Press , 2021 : 1442 - 1447 .

GHOSH R . On-road vehicle detection in varying weather conditions using Faster R-CNN with several region proposal networks [J ] . Multimedia Tools and Applications , 2021 , 80 ( 17 ): 25985 - 25999 .

HSU S C , HUANG C L , CHUANG C H . Vehicle detection using simplified Fast R-CNN [C ] // Proceedings of 2018 International Workshop on Advanced Image Technology (IWAIT) . Piscataway:IEEE Press , 2018 : 1 - 3 .

宁俊 , 王年 , 朱明 . 基于改进Faster R-CNN的车辆类型识别算法 [J ] . 安徽大学学报(自然科学版) , 2021 , 45 ( 3 ): 26 - 33 .

NING J , WANG N , ZHU M . Vehicle type recognition algorithm based on the improved Faster R-CNN [J ] . Journal of Anhui University (Natural Sciences) , 2021 , 45 ( 3 ): 26 - 33 .

LUO J Q , FANG H S , SHAO F M , et al . Multi-scale traffic vehicle detection based on Faster R-CNN with NAS optimization and feature enrichment [J ] . Defence Technology , 2021 , 17 ( 4 ): 1542 - 1554 .

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

GAO X , WANG J F , ZHOU M Z . The research of resource allocation method based on GCN-LSTM in 5G network [J ] . IEEE Communications Letters , 2023 , 27 ( 3 ): 926 - 930 .

ERKAN U , TOKTAS A , USTUN D . Hyperparameter optimization of deep CNN classifier for plant species identification using artificial bee colony algorithm [J ] . Journal of Ambient Intelligence and Humanized Computing , 2023 , 14 ( 7 ): 8827 - 8838 .

ZHANG X L , CUI J , LIU H J , et al . Weed identification in soybean seedling stage based on optimized Faster R-CNN algorithm [J ] . Agriculture , 2023 , 13 ( 1 ): 175 .

DONG Z , WU Y W , PEI M T , et al . Vehicle type classification using a semisupervised convolutional neural network [J ] . IEEE Transactions on Intelligent Transportation Systems , 2015 , 16 ( 4 ): 2247 - 2256 .

WEN L Y , DU D W , CAI Z W , et al . UA-DETRAC:a new benchmark and protocol for multi-object detection and tracking [J ] . Computer Vision and Image Understanding , 2020 ( 193 ): 102907 .

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