基于改进YOLOv3-SPP算法的道路车辆检测

doi:10.11959/j.issn.1000-436x.2024046

Abstract

Abstract:

Aiming at the problem of low detection accuracy or missing detection caused by dense vehicles and small scale of distant vehicles in the visual detection of urban road scenes, an improved YOLOv3-SPP algorithm was proposed to optimize the activation function and take DIOU-NMS Loss as the boundary frame loss function to enhance the expression ability of the network.In order to improve the feature extraction ability of the proposed algorithm for small targets and occluding targets, the void convolution module was introduced to increase the receptive field of the target.Based on the experimental results, the proposed algorithm improves the mAP by 1.79% when detecting vehicle targets, and also effectively reduce the missing phenomenon when detecting tight vehicle targets.

Key words: vehicle detection, YOLOv3-SPP algorithm, activation function, atrous convolution, deep learning

CLC Number:

TN92

Tao WANG, Hao FENG, Rongxin MI, Lin LI, Zhenxue HE, Yiming FU, Shu WU. Road vehicle detection based on improved YOLOv3-SPP algorithm[J]. Journal on Communications, 2024, 45(2): 68-78.

Figures/Tables 9

References 27

[1]	刘耀辉 . 基于深度学习的车辆检测研究[D]. 南京:南京邮电大学, 2022.
	LIU Y H . Research on vehicle detection based on deep learning[D]. Nanjing:Nanjing University of Posts and Telecommunications, 2022.
[2]	JABRI S , SAIDALLAH M , BELRHITI E A A ,et al. Moving vehicle detection using Haar-like,LBP and a machine learning adaboost algorithm[C]// Proceedings of IEEE International Conference on Image Processing,Applications and Systems (IPAS). Piscataway:IEEE Press, 2018: 121-124.
[3]	DAVID H , ATHIRA T A . Improving the performance of vehicle detection and verification by Log Gabor filter optimization[C]// Proceedings of Fourth International Conference on Advances in Computing and Communications. Piscataway:IEEE Press, 2014: 50-55.
[4]	GIRSHICK R , DONAHUE J , DARRELL T ,et al. Rich feature hierarchies for accurate object detection and semantic segmentation[C]// Proceedings of IEEE Conference on Computer Vision and Pattern Recognition. Piscataway:IEEE Press, 2014: 580-587.
[5]	GU R , BILLINGE S J L , DU Q . A fast two-stage algorithm for non-negative matrix factorization in smoothly varying data[J]. Acta Crystallographica Section A,Foundations and Advances, 2023,79(2): 203-216.
[6]	ZHAO W Q , XU M F , CHENG X F ,et al. An insulator in transmission lines recognition and fault detection model based on improved faster RCNN[J]. IEEE Transactions on Instrumentation and Measurement, 2021,70: 1-8.
[7]	LIU W , ANGUELOV D , ERHAN D ,et al. SSD:single shot multibox detector[C]// European Conference on Computer Vision. Berlin:Springer, 2016: 21-37.
[8]	REDMON J , DIVVALA S , GIRSHICK R ,et al. You only look once:unified,real-time object detection[C]// Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Piscataway:IEEE Press, 2016: 779-788.
[9]	REDMON J , FARHADI A . YOLO9000:better,faster,stronger[C]// Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Piscataway:IEEE Press, 2017: 6517-6525.
[10]	SHEN J Q , LIU N Z , SUN H ,et al. Vehicle detection in aerial images based on lightweight deep convolutional network and generative adversarial network[J]. IEEE Access, 2019,7: 148119-148130.
[11]	CARION N , MASSA F , SYNNAEVE G ,et al. End-to-end object detection with transformers[C]// European Conference on Computer Vision. Berlin:Springer, 2020: 213-229.
[12]	QIU L R , ZHANG D B , TIAN Y ,et al. Deep learning-based algorithm for vehicle detection in intelligent transportation systems[J]. The Journal of Supercomputing, 2021,77(10): 11083-11098.
[13]	SUN X C , XIA T R , WANG T Y ,et al. A lightweight neural network for vehicle object detection[C]// Proceedings of IEEE 4th International Conference on Civil Aviation Safety and Information Technology (ICCASIT). Piscataway:IEEE Press, 2022: 1103-1106.
[14]	WU Y F , GUAN X R , ZHAO B Y ,et al. Vehicle detection based on adaptive multimodal feature fusion and cross-modal vehicle index using RGB-T images[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2023,16: 8166-8177.
[15]	XU J , LI Z S , DU B W ,et al. ReLUplex made more practical:leaky ReLU[C]// Proceedings of the IEEE Symposium on Computers and Communications (ISCC). Piscataway:IEEE Press, 2020: 1-7.
[16]	CHEN L C , PAPANDREOU G , KOKKINOS I ,et al. DeepLab:semantic image segmentation with deep convolutional nets,atrous convolution,and fully connected CRFs[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2018,40(4): 834-848.
[17]	李琦林 . 面向目标检测的边界框回归改进算法研究与实现[D]. 长春:吉林大学, 2023.
	LI Q L . Research and implementation of improved bounding box regression algorithm for target detection[D]. Changchun:Jilin University, 2023.
[18]	JIANG K , ITOH H , ODA M ,et al. Gaussian affinity and GIoU-based loss for perforation detection and localization from colonoscopy videos[J]. International Journal of Computer Assisted Radiology and Surgery, 2023,18(5): 795-805.
[19]	ZHENG Z H , WANG P , LIU W ,et al. Distance-IoU loss:faster and better learning for bounding box regression[J]. arXiv Preprint,arXiv:1911.08287, 2019.
[20]	CAO J , SU Z , YU L Y ,et al. Softmax cross entropy loss with unbiased decision boundary for image classification[C]// Proceedings of Chinese Automation Congress (CAC). Piscataway:IEEE Press, 2018: 2028-2032.
[21]	REZATOFIGHI H , TSOI N , GWAK J ,et al. Generalized intersection over union:a metric and a loss for bounding box regression[C]// Proceedings of IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). Piscataway:IEEE Press, 2019: 658-666.
[22]	赵文清, 严海, 邵绪强 . 改进的非极大值抑制算法的目标检测[J]. 中国图象图形学报, 2018,23(11): 1676-1685.
	ZHAO W Q , YAN H , SHAO X Q . Object detection based on improved non-maximum suppression algorithm[J]. Journal of Image and Graphics, 2018,23(11): 1676-1685.
[23]	NI J J , SHEN K , CHEN Y ,et al. An improved SSD-like deep network-based object detection method for indoor scenes[J]. IEEE Transactions on Instrumentation and Measurement, 2023,72: 1-15.
[24]	YANG L , ZHONG J H , ZHANG Y ,et al. An improving faster-RCNN with multi-attention ResNet for small target detection in intelligent autonomous transport with 6G[J]. IEEE Transactions on Intelligent Transportation Systems, 2023,24(7): 7717-7725.
[25]	GAO P , TIAN J W , TAI Y ,et al. Vehicle detection with bottom enhanced RetinaNet in aerial images[C]// Proceedings of IEEE International Geoscience and Remote Sensing Symposium. Piscataway:IEEE Press, 2020: 1173-1176.
[26]	ZHOU S L , XU H , ZHANG G H ,et al. Leveraging deep convolutional neural networks pre-trained on autonomous driving data for vehicle detection from roadside LiDAR data[J]. IEEE Transactions on Intelligent Transportation Systems, 2022,23(11): 22367-22377.
[27]	YANG K L , JIAO Z Y , LIANG J X ,et al. An application case of object detection model based on YOLOv3-SPP model pruning[C]// Proceedings of IEEE International Conference on Artificial Intelligence and Computer Applications (ICAICA). Piscataway:IEEE Press, 2022: 578-582.

Metrics

Recommended 0

No Suggested Reading articles found!

名称	相关配置
中央处理器	AMD Ryzen 7 4800H with Radeon Graphics2.90 GHz
内存	DDR4 3200 MHz EEC Reg 32 GB
GPU	NVIDIA GTX1650ti
GPU加速库	CUDA10.2，CUDNN8.7.0
操作系统	Windows 10家庭中文版
数据处理	Python3.9，OpenCV
深度学习框架	PyTorch1.7.1

参数名称	预设值	作用
图片大小	512×512	保持相同的图片预设处理方式，发挥模型的推理效果
迭代次数	1 000	整个数据集迭代次数
批大小	16	对数据集划分，批量处理数据
IOU阈值	0.7	目标检测可信度
置信度阈值	0.45	判断是否存在检测目标

指标	mAP	AP			FPS/Hz
指标	mAP	小汽车	公交车	摩托车	FPS/Hz
FE-CNN	80.31%	82.60%	79.64%	78.69%	25.69
NanoDet	87.59%	88.72%	85.33%	88.72%	17.61
SSD^[23]	76.98%	80.21%	73.28%	77.45%	11.82
Faster-RCNN^[24]	79.63%	84.67%	69.94%	84.28%	29.56
RetinaNet^[25]	81.92%	85.30%	77.26%	83.19%	35.24
Proposed CNN^[26]	84.37%	86.51%	81.92%	84.68%	47.63
传统的YOLOv3-SPP^[27]	88.66%	92.59%	84.17%	89.23%	52.38
改进的YOLOv3-SPP	90.45%	93.47%	85.56%	92.31%	49.54

Road vehicle detection based on improved YOLOv3-SPP algorithm

RichHTML

PDF

Knowledge

Abstract

Cite this article

share this article

Figures/Tables 9

References 27

Related Articles 15

Metrics

Recommended 0

[1]	Shuwen YU, Wei XU, Jiacheng YAO. Causality adversarial attack generation algorithm for intelligent unmanned communication system [J]. Journal on Communications, 2024, 45(1): 54-62.
[2]	Xuejun ZHANG, Fenghe ZHANG, Jiyang GAI, Xiaogang DU, Wenjie ZHOU, Teli CAI, Bo ZHAO. mVulSniffer: a multi-type source code vulnerability sniffer method [J]. Journal on Communications, 2023, 44(9): 149-160.
[3]	Mian LI, Yang LI, Zonghui ZHANG, Qingjiang SHI. Communication-efficient distributed precoding design for Massive MIMO [J]. Journal on Communications, 2023, 44(8): 37-48.
[4]	Huijiao WANG, Xin ZHANG, Yongzhuang WEI, Lingchen LI. Novel distinguisher for SM4 cipher algorithm based on deep learning [J]. Journal on Communications, 2023, 44(7): 171-184.
[5]	Rongpeng LI, Bingyan WANG, Honggang ZHANG, Zhifeng ZHAO. Design of knowledge enhanced semantic communication receiver [J]. Journal on Communications, 2023, 44(6): 70-76.
[6]	Dongyu CHEN, Hua CHEN, Limin FAN, Yifang FU, Jian WANG. Research on test strategy for randomness based on deep learning [J]. Journal on Communications, 2023, 44(6): 23-33.
[7]	Shuai MA, Ke PEI, Huayan QI, Hang LI, Wen CAO, Hongmei WANG, Hailiang XIONG, Shiyin LI. Research on geomagnetic indoor high-precision positioning algorithm based on generative model [J]. Journal on Communications, 2023, 44(6): 211-222.
[8]	Yun ZHANG, Jing ZHOU, Jingwei HUANG, Shujuan YU, Liya HUANG. Channel estimation for OFDM system based on deep learning [J]. Journal on Communications, 2023, 44(12): 124-133.
[9]	Gang XIE, Quanyi WANG, Xinlin XIE, Jian’an WANG. Lightweight Transformer traffic scene semantic segmentation algorithm integrating multi-scale depth convolution [J]. Journal on Communications, 2023, 44(10): 213-225.
[10]	Jie YANG, Biao DONG, Xue FU, Yu WANG, Guan GUI. Lightweight decentralized learning-based automatic modulation classification method [J]. Journal on Communications, 2022, 43(7): 134-142.
[11]	Xiuzhang YANG, Guojun PENG, Zichuan LI, Yangqi LYU, Side LIU, Chenguang LI. Research on entity recognition and alignment of APT attack based on Bert and BiLSTM-CRF [J]. Journal on Communications, 2022, 43(6): 58-70.
[12]	Yurong LIAO, Haining WANG, Cunbao LIN, Yang LI, Yuqiang FANG, Shuyan NI. Research progress of deep learning-based object detection of optical remote sensing image [J]. Journal on Communications, 2022, 43(5): 190-203.
[13]	Yong LIAO, Shiyi WANG. CSI feedback algorithm based on RM-Net for massive MIMO systems in high-speed mobile environment [J]. Journal on Communications, 2022, 43(5): 166-176.
[14]	Zenghua ZHAO, Yuefan TONG, Jiayang CUI. Device-independent Wi-Fi fingerprinting indoor localization model based on domain adaptation [J]. Journal on Communications, 2022, 43(4): 143-153.
[15]	Yong LIAO, Gang CHENG, Yujie LI. CSI feedback algorithm based on deep unfolding for massive MIMO systems [J]. Journal on Communications, 2022, 43(12): 77-88.