Intelligent Vehicle Target Fast Recognition Based on Lightweight
Network and Attention Mechanism

doi:10.16097/j.cnki.1009-6744.2022.06.011

Abstract

Abstract: To improve the real-time detection ability of intelligent vehicle in real environment and improve the detection effect in complex environment, this paper proposes a target fast recognition method of intelligent vehicle based on lightweight network and attention mechanism. The GhostNet is proposed to accelerate the feature extraction of YOLOv4, which also helps to reduce the network calculation parameters and improve the reasoning speed of the target recognition algorithm. Then, an improved attention module combined with soft thresholding is added to GhostNet and feature pyramid to improve the recognition accuracy of road targets in complex scenarios. The Pascal VOC, KITTI public data sets and some assumed urban road datasets are selected for experimental comparison to verify the effectiveness of the proposed method. Compared with other target detection algorithms in terms of accuracy and speed, the average detection accuracy of this method is increased by 1.7%, the model parameters are reduced to 18.7% of the original, and the detection speed is increased by 66% . The detection speed and accuracy from the proposed method are higher than the traditions algorithms, which can meet the real-time perception needs of intelligent vehicles.

Key words: intelligent transportation, target recognition, deep learning, lightweight, attention mechanism

摘要： 为提高智能车在真实环境中的实时检测能力，改善复杂环境下检测效果不佳的问题，本文提出一种基于轻量化网络和注意力机制的智能车快速目标识别方法。首先，为了减少网络计算参数和提升目标识别算法的推理速度，提出利用GhostNet加速YOLOv4的特征提取；其次，为了提高复杂场景下对道路目标的识别精度，在GhostNet和特征金字塔部分添加结合软阈值化改进的注意力模块；最后，为了验证本文提出方法的有效性，选取Pascal VOC、KITTI公开数据集和自制城市道路数据集进行实验对比。与其他目标检测算法在精度和速度上进行比较，结果证明，本文方法在平均检测精度提升1.7%的情况下，模型参数量降低到原来的18.7%，检测速度提升了 66%，检测速度和精度均优于其他算法，可满足智能车的实时感知需求。

关键词: 智能交通, 目标识别, 深度学习, 轻量化, 注意力机制

CLC Number:

TP391.4

CHEN Zhi-jun, HU Jun-nan, LENG Yao, QIAN Chuang, WU Chao-zhong. Intelligent Vehicle Target Fast Recognition Based on Lightweight Network and Attention Mechanism[J]. Journal of Transportation Systems Engineering and Information Technology, 2022, 22(6): 105-113.

陈志军, 胡军楠, 冷姚, 钱闯, 吴超仲. 基于轻量化网络和注意力机制的智能车快速目标识别方法[J]. 交通运输系统工程与信息, 2022, 22(6): 105-113.

Add to citation manager EndNote|Ris|BibTeX

URL: http://www.tseit.org.cn/EN/10.16097/j.cnki.1009-6744.2022.06.011

http://www.tseit.org.cn/EN/Y2022/V22/I6/105

References

[1] 胡笳, 罗书源, 赖金涛, 等. 自动驾驶对交通运输系统规划的影响综述[J]. 交通运输系统工程与信息, 2021, 21(5): 52-65. [HU J, LUO S Y, LAI J T, et al. A review of the impact of autonomous driving on transportation planning[J]. Journal of Transportation Systems Engineering and Information Technology, 2021, 21(5): 52-65.]

[2] 李克强, 戴一凡, 李升波, 等. 智能网联汽车(ICV)技术的发展现状及趋势[J]. 汽车安全与节能学报, 2017, 8 (1): 1-14. [LI K Q, DAI Y F, LI S B, et al. Development status and trend of intelligent connected vehicle (ICV) technology[J]. Journal of Automotive Safety and Energy, 2017, 8(1): 1-14.]

[3] REDMON J, FARHADI A. Yolov3: An incremental improvement[J]. ArXiv Preprint ArXiv: 1804.02767, 2018.

[4] REN S, HE K, GIRSHICK R, et al. Faster RCNN: Towards real- time object detection with region proposal networks[J]. Advances in Neural Information Processing Systems, 2015, 38(6): 1137-1149.

[5] POSSATTI L C, GUIDOLINI R, CARDOSO V B, et al. Traffic light recognition using deep learning and prior maps for autonomous cars[C]//2019 International Joint Conference on Neural Networks (IJCNN), IEEE, 2019: 1-8.

[6] MOHD-ISA W N, ABDULLAH M S, SARZIL M, et al. Detection of malaysian traffic signs via modified YOLOv3 algorithm[C]//2020 International Conference on Data Analytics for Business and Industry: Way Towards a Sustainable Economy (ICDABI), IEEE, 2020: 1-5.

[7] 邵毅明, 屈治华, 邓天民, 等. 基于CapsNet的行人检测方法及评价[J]. 交通运输系统工程与信息, 2019, 19 (3): 54- 61. [SHAO Y M, QU Z H, DENG T M, et al. Pedestrian detection method and evaluation based on Capsnet[J]. Journal of Transportation Systems Engineering and Information Technology, 2019, 19(3): 54-61.]

[8] HOWARD A, ZHU M, CHEN B, et al. Mobilenets: Efficient convolutional neural networks for mobile vision applications[J]. Arxiv Preprint ArXiv, 2017: 1704.04861.

[9] ZHANG X, ZHOU X, LIN M, et al. Shufflenet: An extremely efficient convolutional neural network for mobile devices[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2018: 6848-6856.

[10] DENG T, WU Y. Simultaneous vehicle and lane detection via MobileNetV3 in car following scene[J]. Plos One, 2022, 17(3): e0264551.

[11] BOCHKOVSKIY A, WANG C Y, LIAO H. YOLOv4: optimal speed and accuracy of object detection[C]//IEEE Conference on Computer Vision and Pattern Recognition (CVPR)，2020, arXiv: 2004.

[12] 史宝岱, 张秦, 李瑶, 等. 基于轻量化卷积神经网络的图像目标识别算法[J/OL]. 计算机工程, 2022, 48(6): 257-262. [SHI B D, ZHANG Q, LI Y, et al. Image target recognition algorithm based on lightweight convolution neural network[J/OL]. Computer Engineering, 2022, 48 (6): 257-262.]

[13] 张凡. 基于轻量化多尺度特征融合与注意力机制的交通标志检测与识别 [D]. 西安: 长安大学, 2021. [ZHANG F. Traffic sign detection and recognition based on lightweight multi-scale feature fusion and attention mechanism [D]. Xi'an: Chang'an University, 2021.]

[14] CHEN S, LIN W. Embedded system real-time vehicle detection based on improved YOLO network[C]//2019 IEEE 3RD Advanced Information Management, Communicates, Electronic and Automation Control Conference (IMCEC), IEEE, 2019: 1400-1403.

[15] HAN K, WANG Y, TIAN Q, et al. Ghostnet: More features from cheap operations[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2020: 1580-1589.

[16] WOO S, PARK J, LEE J Y, et al. Cbam: Convolutional block attention module[C]//Proceedings of the European Conference on Computer Vision (ECCV), 2018: 3-19.

[17] LIU W, ANGUELOV D, ERHAN D, et al. SSD: Single shot multibox detector[C]//European Conference on Computer Vision, Springer, Cham, 2016: 21-37.

Intelligent Vehicle Target Fast Recognition Based on Lightweight Network and Attention Mechanism

基于轻量化网络和注意力机制的智能车快速目标识别方法

PDF

Knowledge

Abstract

Cite this article

share this article

References

Related Articles 15

Recommended Articles

Metrics

[1]	ZHAO Jian-dong, HE Xiao-yu, YU Zhi-xin, HAN Ming-min. A Combination Model for Connected and Autonomous Vehicles Lane-changing Decision-making Under Multi Connectivity Range [J]. Journal of Transportation Systems Engineering and Information Technology, 2023, 23(1): 77-85.
[2]	SUN Wei, ZHANG Meng-ya, MA Cheng-yuan, ZHU Ji-chen, YANG Xiao-guang. Coordination of Signal and Vehicle Trajectory at Intersections for Mixed Traffic Flow [J]. Journal of Transportation Systems Engineering and Information Technology, 2023, 23(1): 97-105.
[3]	CHEN Xi-qun, ZHU Yi-zhang, LV Chao-feng. Signal Phase and Timing Optimization Method for Intersection Based on Hybrid Proximal Policy Optimization [J]. Journal of Transportation Systems Engineering and Information Technology, 2023, 23(1): 106-113.
[4]	HE Wen-wu, PEI Bo-yu, LI Ya-ting, LIU Xiao-yu, XU Shao-bing. Traffic Flow Forecasting Based on Bi-directional Adaptive Gating Graph Convolutional Networks [J]. Journal of Transportation Systems Engineering and Information Technology, 2023, 23(1): 187-197.
[5]	GAO Yu-hong, , QU Zhao-wei , SONG Xian-min . Car Operation Rate Prediction Based on Combination Model of Hunter-prey Optimizer Algorithm and Bi-directional Long Short-term Memory Neural Network [J]. Journal of Transportation Systems Engineering and Information Technology, 2023, 23(1): 198-206.
[6]	HAO Wei, GONG Ya-xin, ZHANG Zhao-lei, YI Ke-fu. Cooperative Merging Strategy for Freeway Ramp in a Mixed Traffic Environment [J]. Journal of Transportation Systems Engineering and Information Technology, 2023, 23(1): 224-235.
[7]	XIE Xin-lin, LUO Chen-yan, XU Xin-ying, XIE Gang. Dual Attention Guided Cross-layer Optimized Traffic Scene Semantic Segmentation [J]. Journal of Transportation Systems Engineering and Information Technology, 2023, 23(1): 236-244.
[8]	YANG Liu, YANG Ying, SONG Yun-zhou, ZHANG Yu. A Review of Influencing Factors and Identification Methods of Driver Stress [J]. Journal of Transportation Systems Engineering and Information Technology, 2022, 22(6): 40-50.
[9]	XIANG Xin-jian, HU Hai-bin, YAO Jia-na, DING Yi, ZHENG Yong-ping, JIN Li. A Road Small Target Tracking Approach Based on Object-shadow Matching Algorithm [J]. Journal of Transportation Systems Engineering and Information Technology, 2022, 22(6): 85-94.
[10]	ZHU Xing-lin, YAO Liang, LIU Hong-jun, ERASEL·Kuken, ELI·Ismutulla. Identification of Dangerous Sections of Highland Roads Considering Different Driving Behaviors [J]. Journal of Transportation Systems Engineering and Information Technology, 2022, 22(6): 172-182.
[11]	ZHANG Wen-juan, YANG Hao-zhe, ZHANG Bin, LI Xiu-jie. Short-time Passenger Flow Prediction Model of Urban Rail Transit Considering Multi-timescale Features [J]. Journal of Transportation Systems Engineering and Information Technology, 2022, 22(6): 212-223.
[12]	PENG Hong-qin , ZHANG Guo-wu. Theoretical Integration and Practice of System Engineering and Transportation in the New Era [J]. Journal of Transportation Systems Engineering and Information Technology, 2022, 22(5): 1-6.
[13]	LIU Jiang, TAN Si-lun, CAI Bai-gen, WANG Jian . Cooperative Weighting Method for Satellite-based Vehicle Positioning Based on Vehicle Infrastructure Information Interaction [J]. Journal of Transportation Systems Engineering and Information Technology, 2022, 22(5): 85-96.
[14]	ZHANG Ming-fang, WU Yu-feng, WANG Li, WANG Pang-wei. Pyramid-feature-fusion-based Two-stage Vehicle Detection via 3D Point Cloud [J]. Journal of Transportation Systems Engineering and Information Technology, 2022, 22(5): 107-116.
[15]	ZHANG Ze-xi, ZHONG Wen-jian, LIN Bo-liang. Optimization of Truck Platooning Routing with Time Windows [J]. Journal of Transportation Systems Engineering and Information Technology, 2022, 22(5): 253-263.