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Object Detection Method of Autonomous Vehicle Based on Lightweight Deep Learning
Technical Paper
2021-01-0192
ISSN: 0148-7191, e-ISSN: 2688-3627
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Event:
SAE WCX Digital Summit
Language:
English
Abstract
Object detection is an important visual content of the autonomous vehicle, the traditional detecting methods usually cost a lot of computational memory and elapsed time. This paper proposes to use lightweight deep convolutional neural network (MobilenetV3-SSDLite) to carry out the object detection task of autonomous vehicles. Simulation analysis based on this method is implemented, the feature layer obtained after h-swish activation function in the first Conv of the 13th bottleneck module in MobilenetV3 is taken as the first effective feature layer, and the feature layer before pooling and convolution of the antepenultimate layer in MobilenetV3 is taken as the second effective feature layer, and these two feature layers are extracted from the MobilenetV3 network. The two output effective feature layers of MobilenetV3 are used to replace the two output feature layers of VGG16 in the SSD network, and the rest convolutions of SSD was replaced by lightweight separable convolutions and inverted residual structures, thus the architecture of Mobilenetv3-SSDLite is completed. It trained 800 epochs on RTX 2080 using KITTI data, and its training and detecting performance is recorded. In order to be evaluated, this method is compared with the original SSD networks from training and detecting performance. Results show that both MobilenetV3-SSDLite and original SSD networks can detect the pedestrians and vehicles effectively, but MobilenetV3-SSDLite network can greatly improve the detecting speed and save the hardware cost by only slightly sacrificing the detecting accuracy, therefore, it is more suitable for mobile platform such as the autonomous vehicle, and then it is more suitable for object detection task of the autonomous vehicle.
Authors
Topic
Citation
Guo, R. and Xie, X., "Object Detection Method of Autonomous Vehicle Based on Lightweight Deep Learning," SAE Technical Paper 2021-01-0192, 2021, https://doi.org/10.4271/2021-01-0192.Data Sets - Support Documents
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References
- Dalal , N. and Triggs , B. Histograms of Oriented Gradients for Human Detection 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) San Diego, CA, USA 2005 886 893 1 10.1109/CVPR.2005.177
- Lowe , D.G. Object Recognition from Local Scale-Invariant Features Proceedings of the International Conference on Computer Vision 1999 1150 1157
- Kazemi , F.M. , Samadi , S. , Poorreza , H.R. , and Akbarzadeh-T , M. Vehicle Recognition Using Curvelet Transform and SVM Fourth International Conference on Information Technology (ITNG'07) Las Vegas, NV 2007 516 521 10.1109/ITNG.2007.205
- Kuang , X. , Wang , C. , Xu , L. and Xiao , D. Detection of Two-Wheeled Vehicles Based on Gaussian Mixture Model and AdaBoost Algorithm Proceeding of the 11th World Congress on Intelligent Control and Automation Shenyang 2014 3359 3363 10.1109/WCICA.2014.7053272
- Gao , Y. , Chen , M. , and Ma , L. Vehicle Detection Segmentation Based on Adaboost and Grabcut 2010 IEEE International Conference on Progress in Informatics and Computing Shanghai 2010 896 900 10.1109/PIC.2010.5687896
- Zhang , Y. and He , P. A Revised AdaBoost Algorithm: FM-AdaBoost 2010 International Conference on Computer Application and System Modeling (ICCASM 2010) Taiyuan 2010 V11-277 V11-281 10.1109/ICCASM.2010.5623209
- Girshick , R. , Donahue , J. , Darrell , T. , and Malik , J. Rich Feature Hierarchies for Accurate Object Detection and Semantic Segmentation 2014 IEEE Conference on Computer Vision and Pattern Recognition Columbus, OH 2014 580 587 10.1109/CVPR.2014.81
- Girshick , R. Fast R-CNN 2015 IEEE International Conference on Computer Vision (ICCV) Santiago 2015 1440 1448 10.1109/ICCV.2015.169
- Ren , S. , He , K. , Girshick , R. , and Sun , J. Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks IEEE Transactions on Pattern Analysis and Machine Intelligence 39 6 1137 1149 1 June 2017 10.1109/TPAMI.2016.2577031
- Redmon , J. , Divvala , S. , Girshick , R. , and Farhadi , A. You Only Look Once: Unified, Real-Time Object Detection 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Las Vegas, NV 2016 779 788 10.1109/CVPR.2016.91
- Liu , W. , Anguelov , D. , Erhan , D. et al. Ssd: Single Shot Multibox Detector[C]//European Conference on Computer Vision Cham Springer 2016 21 37
- Howard , A. et al. Searching for MobileNetV3 2019 IEEE/CVF International Conference on Computer Vision (ICCV) Seoul, Korea (South) 2019 1314 1324 10.1109/ICCV.2019.00140
- Howard , A.G. , Zhu , M. , Chen , B. , Kalenichenko , D. et al. 2017
- Sandler , M. , Howard , A. , Zhu , M. , Zhmoginov , A. , and Chen , L. MobileNetV2: Inverted Residuals and Linear Bottlenecks 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition Salt Lake City, UT 2018 4510 4520 10.1109/CVPR.2018.00474