Adaptive collaborative optimization algorithm for vehicle recognition based on FIPA model

doi:10.11772/j.issn.1001-9081.2024010035

Journal of Computer Applications ›› 0, Vol. ›› Issue (): 267-273.DOI: 10.11772/j.issn.1001-9081.2024010035

• Multimedia computing and computer simulation • Previous Articles Next Articles

Adaptive collaborative optimization algorithm for vehicle recognition based on FIPA model

Kaiming WANG¹^,², Zhehao LUO²^,³, Wenqi SUN²^,³, Guoyuan LIANG²(), Yong LIANG¹

^1.College of Mechanical and Control Engineering，Guilin University of Technology，Guilin Guangxi 541006，China
^2.Guangdong Provincial Key Laboratory of Robotics and Intelligent System （Shenzhen Institute of Advanced Technology，Chinese Academy of Sciences），Shenzhen Guangdong 518055，China
^3.University of Chinese Academy of Sciences，Beijing 100049，China

Received:2024-01-16 Revised:2024-04-09 Accepted:2024-04-10 Online:2024-05-09 Published:2024-12-31
Contact: Guoyuan LIANG

基于FIPA模型的自适应协同优化的车型识别算法

王开明¹^,², 罗哲皓²^,³, 孙文琪²^,³, 梁国远²(), 梁勇¹

^1.桂林理工大学机械与控制工程学院，广西桂林 541006
^2.广东省机器人与智能系统重点实验室（中国科学院深圳先进技术研究院），广东深圳 518055
^3.中国科学院大学，北京 100049

通讯作者: 梁国远
作者简介:王开明（1998—），男，安徽安庆人，硕士研究生，主要研究方向：计算机视觉
罗哲皓（1995—），男，江西萍乡人，硕士研究生，主要研究方向：计算机视觉
孙文琪（2001—），女，山东淄博人，硕士研究生，主要研究方向：计算机图形学
梁国远（1974—），男，广西北流人，副研究员，博士，主要研究方向：计算机视觉、模式识别
梁勇（1981—），男，湖北武汉人，副教授，博士，主要研究方向：智能机器人、SLAM、FPGA、边缘计算、深度学习、目标检测。
基金资助:
中国科学院科技服务网络计划项目（STS）专项(STS-HP-202201)

Abstract

Abstract:

In view of the challenges encountered in vehicle recognition tasks， including the artificial combinations ignoring the information of other parts and the precision decline caused by inconsistent values in multi-network fusion， an adaptive collaborative optimization algorithm based on Foundation for Intelligent Physical Agents （FIPA） model was proposed for vehicle recognition. Firstly， in the feature extraction process， SPace-to-Depth-Convolution （SPD-Conv） was used to replace the standard convolutional layer in YOLOv8 to solve the problem of fine-grained information loss and ineffective detection. Secondly， an adaptive collaborative optimization network was designed to mine the vehicle parts carrying effective information and solve the problem of disordered competition among agents. Finally， a weighted log-polar voting mechanism based on FIPA model was introduced to integrate the short-distance and long-distance fine-grained information to solve the problem of precision decline caused by inconsistent values of agents in the fusion process. Experimental results on DeepCar5.0 dataset show that compared with YOLOv5， the mean Average Precision （mAP） with Intersection over Union （IoU） of 0.5 of the proposed algorithm is improved by 1.80 percentage points in the object detection stage； in the classification fusion stage， the classification accuracy of the proposed algorithm is improved by 6.48 percentage points， and the classification accuracy is further improved by 7.53 percentage points through adding the preprocessing block.

Key words: vehicle recognition, collaborative optimization, feature extraction, log-polar voting, fine-grained information

摘要：

针对车型识别任务中存在的问题，包括人为组合忽略了其他部位信息以及多网络融合时价值不一致导致的精度下降等，提出一种基于FIPA （Foundation for Intelligent Physical Agents）模型的自适应协同优化算法用于车型识别。首先，在特征提取过程中采用空间到深度卷积层（SPD-Conv）替代YOLOv8中的标准卷积层，以解决细粒度信息丢失和无效检测问题；其次，设计了一种自适应协同优化网络来挖掘携带有效信息的汽车部件，并解决多智能体无序竞争问题；最后，引入基于FIPA模型的加权对数极坐标投票模块来整合近距离和远距离位置上的细粒度信息，从而解决融合过程中多智能体价值不一致所导致的精度下降问题。在DeepCar5.0数据集上的实验结果表明，在目标检测阶段相较于YOLOv5，所提算法在交并比（IoU）为0.5时的平均精度均值（mAP）提高了1.80个百分点；在分类融合阶段，所提算法的分类准确率提高了6.48个百分点，并通过增加预处理块将分类准确率又提高了7.53个百分点。

关键词: 车型识别, 协同优化, 特征提取, 对数极坐标投票, 细粒度信息

CLC Number:

TP391.4

Kaiming WANG, Zhehao LUO, Wenqi SUN, Guoyuan LIANG, Yong LIANG. Adaptive collaborative optimization algorithm for vehicle recognition based on FIPA model[J]. Journal of Computer Applications, 0, (): 267-273.

王开明, 罗哲皓, 孙文琪, 梁国远, 梁勇. 基于FIPA模型的自适应协同优化的车型识别算法[J]. 《计算机应用》唯一官方网站, 0, (): 267-273.

Figures/Tables 15

References 27

1	KRIZHEVSKY A， SUTSKEVER I， HINTON G E. ImageNet classification with deep convolutional neural networks ［J］. Communications of the ACM， 2017， 60（6）： 84-90.
2	HE K， ZHANG X， REN S， et al. Deep residual learning for image recognition ［C］// Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition. Piscataway： IEEE， 2016： 770-778.
3	AMIRKHANI A， BARSHOOI A H. DeepCar 5.0： vehicle make and model recognition under challenging conditions ［J］. IEEE Transactions on Intelligent Transportation Systems， 2023， 24（1）： 541-553.
4	WANG H， PENG J， ZHAO Y， et al. Multi-path deep CNNs for fine-grained car recognition ［J］. IEEE Transactions on Vehicular Technology， 2020， 69（10）： 10484-10493.
5	LIU C， HUANG L， WEI Z， et al. Subtler mixed attention network on fine-grained image classification ［J］. Applied Intelligence， 2021， 51（11）： 7903-7916.
6	DING Y， MA Z， WEN S， et al. AP-CNN： weakly supervised attention pyramid convolutional neural network for fine-grained visual classification ［J］. IEEE Transactions on Image Processing， 2021， 30： 2826-2836.
7	BELLIFEMINE F， POGGI A， RIMASSA G. Developing multi-agent systems with a FIPA-compliant agent framework ［J］. Software： Practice and Experience， 2001， 31（2）： 103-128.
8	LOWE D G. Distinctive image features from scale-invariant keypoints ［J］. International Journal of Computer Vision， 2004， 60（2）： 91-110.
9	CORTES C， VAPNIK V. Support-vector networks ［J］. Machine Learning， 1995， 20（3）： 273-297.
10	HE H， SHAO Z， TAN J. Recognition of car makes and models from a single traffic-camera image ［J］. IEEE Transactions on Intelligent Transportation Systems， 2015， 16（6）： 3182-3192.
11	ABDELMASEEH M， BADRELDIN I， ABDELKADER M F， et al. Car make and model recognition combining global and local cues ［C］// Proceedings of the 21st International Conference on Pattern Recognition. Piscataway： IEEE， 2012： 910-913.
12	HU Q， WANG H， TENG L， et al. Deep CNNs with spatially weighted pooling for fine-grained car recognition ［J］. IEEE Transactions on Intelligent Transportation Systems， 2017， 18（11）： 3147-3156.
13	LIN T Y， RoyCHOWDHURY A， MAJI S. Bilinear CNN models for fine-grained visual recognition ［C］// Proceedings of the 2015 IEEE International Conference on Computer Vision. Piscataway： IEEE， 2015： 1449-1457.
14	LIU D， ZHAO L， WANG Y， et al. Learn from each other to classify better： cross-layer mutual attention learning for fine-grained visual classification ［J］. Pattern Recognition， 2023， 140： No.109550.
15	REDMON J， DIVVALA S， GIRSHICK R， et al. You only look once： unified， real-time object detection ［C］// Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition. Piscataway： IEEE， 2016： 779-788.
16	LIU W， ANGUELOV D， ERHAN D， et al. SSD： single shot multibox detector ［C］// Proceedings of the 2016 European Conference on Computer Vision， LNCS 9905. Cham： Springer， 2016： 21-37.
17	GIRSHICK R， DONAHUE J， DARRELL T， et al. Rich feature hierarchies for accurate object detection and semantic segmentation ［C］// Proceedings of the 2014 IEEE Conference on Computer Vision and Pattern Recognition. Piscataway： IEEE， 2014： 580-587.
18	SUNKARA R， LUO T. No more strided convolutions or pooling： a new CNN building block for low-resolution images and small objects ［C］// Proceedings of the 2022 Joint European Conference on Machine Learning and Knowledge Discovery in Databases， LNCS 13715. Cham： Springer， 2023： 443-459.
19	SORIA X， RIBA E， SAPPA A. Dense extreme inception network： towards a robust CNN model for edge detection ［C］// Proceedings of the 2020 IEEE Winter Conference on Applications of Computer Vision. Piscataway： IEEE， 2020： 1912-1921.
20	HUANG X， BELONGIE S. Arbitrary style transfer in real-time with adaptive instance normalization ［C］// Proceedings of the 2017 IEEE International Conference on Computer Vision. Piscataway： IEEE， 2017： 1510-1519.
21	SAGI O， ROKACH L. Ensemble learning： a survey ［J］. WIREs： Data Mining and Knowledge Discovery， 2018， 8（4）： No.e1249.
22	FORSTER K I， BEDNALL E S. Terminating and exhaustive search in lexical access ［J］. Memory and Cognition， 1976， 4（1）： 53-61.
23	JOCHER G， CHAURASIA A， STOKEN A， et al. Ultralytics/YOLOv5： v7.0 — YOLOv5 SOTA realtime instance segmentation ［EB/OL］. ［2024-03-26］. .
24	Ultralytics. Ultralytics ［EB/OL］. ［2024-03-26］. .
25	RAO Y， CHEN G， LU J， et al. Counterfactual attention learning for fine-grained visual categorization and re-identification ［C］// Proceedings of the 2021 IEEE/CVF International Conference on Computer Vision. Piscataway： IEEE， 2021： 1005-1014.
26	HUTTUNEN H， YANCHESHMEH F S， CHEN K. Car type recognition with deep neural networks ［C］// Proceedings of the 2016 IEEE Intelligent Vehicles Symposium. Piscataway： IEEE， 2016： 1115-1120.
27	XIANG Y， FU Y， HUANG H. Global topology constraint network for fine-grained vehicle recognition ［J］. IEEE Transactions on Intelligent Transportation Systems， 2020， 21（7）： 2918-2929.

智能体	框架	网络架构	学习率	学习率衰减	权重衰减	轮次	批量大小	正则化	优化器	预训练权重	损失函数	学习率调度器	随机排列
头灯	PyTorch	ResNet101	0.000 05	30	0.000 1	150	16	Dropout	Adam	ImageNet	Cross Entropy	StepLR	TRUE
上栏栅	PyTorch	VGG19	0.000 05	30	0.000 5	145	16	Dropout	Adam	ImageNet	Cross Entropy	StepLR	TRUE
保险杠	PyTorch	Xception	0.000 30	—	0.000 4	150	8	Dropout	SGD	ImageNet	Cross Entropy	CycliLR	TRUE
雾灯	PyTorch	DenseNet201	0.003 00	—	0.000 5	145	32	Dropout	SGD	ImageNet	Cross Entropy	CycliLR	TRUE
前身	PyTorch	ResNet101	0.005 00	5	—	150	32	Dropout	SGD	ImageNet	Cross Entropy	StepLR	TRUE
车标	PyTorch	VGG19	0.005 00	5	—	150	32	Dropout	SGD	ImageNet	Cross Entropy	StepLR	TRUE
进气口	PyTorch	ResNet101	0.005 00	5	—	150	32	Dropout	SGD	ImageNet	Cross Entropy	StepLR	TRUE

智能体	框架	网络架构	学习率	学习率衰减	权重衰减	轮次	批量大小	正则化	优化器	预训练权重	损失函数	学习率调度器	随机排列
头灯	PyTorch	ResNet101	0.000 05	30	0.000 1	150	16	Dropout	Adam	ImageNet	Cross Entropy	StepLR	TRUE
上栏栅	PyTorch	VGG19	0.000 05	30	0.000 5	145	16	Dropout	Adam	ImageNet	Cross Entropy	StepLR	TRUE
保险杠	PyTorch	Xception	0.000 30	—	0.000 4	150	8	Dropout	SGD	ImageNet	Cross Entropy	CycliLR	TRUE
雾灯	PyTorch	DenseNet201	0.003 00	—	0.000 5	145	32	Dropout	SGD	ImageNet	Cross Entropy	CycliLR	TRUE
前身	PyTorch	ResNet101	0.005 00	5	—	150	32	Dropout	SGD	ImageNet	Cross Entropy	StepLR	TRUE
车标	PyTorch	VGG19	0.005 00	5	—	150	32	Dropout	SGD	ImageNet	Cross Entropy	StepLR	TRUE
进气口	PyTorch	ResNet101	0.005 00	5	—	150	32	Dropout	SGD	ImageNet	Cross Entropy	StepLR	TRUE

类别	配置
系统	Ubuntu20.04
GPU	NVIDIA GeForce RTX4090
CPU	AMD Ryzen Threadripper 5995 Workstation Extreme
脚本语言	Python3.8
内存大小	1 TB

类别	配置
系统	Ubuntu20.04
GPU	NVIDIA GeForce RTX4090
CPU	AMD Ryzen Threadripper 5995 Workstation Extreme
脚本语言	Python3.8
内存大小	1 TB

检测算法	平均精度均值（mAP50）/%
YOLOv5^［23］	95.00
YOLOv8^［24］	95.70
YOLOv8-SPD-Conv	96.80

Adaptive collaborative optimization algorithm for vehicle recognition based on FIPA model

基于FIPA模型的自适应协同优化的车型识别算法

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Figures/Tables 15

References 27

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Metrics

Agent	分类网络	分类准确率Top1		分类准确率Top5
Agent	分类网络	未加预处理块	预处理块	未加预处理块	预处理块
MAS	融合模型	82.48	90.01	93.27	96.67
头灯	ResNet101	61.16	63.84	80.28	80.75
上栏栅	VGG19	64.20	70.01	79.63	83.33
保险杠	Xception	64.74	69.95	77.75	82.26
雾灯	DenseNet201	51.09	—	65.24	—
前身	ResNet101	72.39	82.52	88.17	91.34
车标	VGG19	33.82	—	62.88	—
进气口	ResNet50	18.00	—	30.33	—

算法	分类准确率/%
ResNet101+Counterfactual Attention Learning^［25］	56.00
Deep Neural Network + SVM^［26］	74.00
CNN+Global Topology Constraint^［27］	79.50
MAS+Ensembling^［3］	81.50
本文算法	90.01

DeepCar5.0修订版	SPD-Conv	协同优化	投票机制改进	分类准确率
DeepCar5.0修订版	SPD-Conv	协同优化	投票机制改进	未加预处理块	加预处理块
				76.00	81.50
√				79.36	84.04
√	√			79.97	84.55
√		√		81.14	88.98
√			√	80.09	84.56
√	√	√	√	82.48	90.01

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