计算机科学 ›› 2021, Vol. 48 ›› Issue (1): 89-95.doi: 10.11896/jsjkx.200800034
所属专题： 智能化边缘计算
单美静, 秦龙飞, 张会兵
SHAN Mei-jing, QIN Long-fei, ZHANG Hui-bing
摘要： 在车载边缘计算单元中,由于其硬件设备的资源受限,开发适用于车载边缘计算的轻量级、高效的交通标识检测模型变得越来越迫切。文中提出了一种基于Tiny YOLO改进的轻量级交通标识检测模型,称为L-YOLO。首先,L-YOLO使用部分残差连接来增强轻量级网络的学习能力;其次,为了降低交通标识的误检和漏检,L-YOLO使用高斯损失函数作为边界框的定位损失。在TAD16K交通标识检测数据集上,L-YOLO的参数量为18.8 M,计算量为8.211 BFlops,检测速度为83.3 FPS,同时mAP达到86%。实验结果显示,该算法在保证实时性的同时,还提高了检测精度。
中图分类号:
| [1] ZHU Z,LIANG D,ZHANG S,et al.Traffic-sign detection and classification in the wild[C]//IEEE Conference on Computer Vision and Pattern Recognition.IEEE Computer Society,2016:2110-2118. [2] LI Y,WANG J,XING T,et al.TAD16K:An enhanced benchmark for autonomous driving[C]//IEEE International Confe-rence on Image Processing.IEEE Computer Society,2017:2344-2348. [3] YANG T,LONG X,SANGAIAH A K,et al.Deep detectionnetwork for real-life traffic sign in vehicular networks[J].Computer Networks,2018,136(8):95-104. [4] LU Y,JIA M,ZHANG S,et al.Traffic signal detection and classification in street views using an attention model[J].Computational Visual Media,2018,4(3):253-266. [5] REDMON J,DIVVALA S,GIRSHICK R,et al.You only look once:unified,real-time object detection[C]//IEEE Conference on Computer Vision And Pattern Recognition.IEEE Computer Society,2016:779-788. [6] REDMON J,FARHADI A.YOLO9000:Better,Faster,Stronger[C]//IEEE Conference on Computer Vision and Pattern Recognition.IEEE Computer Society,2017:7263-7271. [7] REDMON J,FARHADI A.YOLOv3:An Incremental Improvement[J].arXiv:1804.02767,2018. [8] WANG C Y,LIAO H Y M,CHEN P Y,et al.Enriching variety of layer-wise learning information by gradient combination[C]//IEEE/CVF International Conference on Computer Vision Workshop.IEEE Computer Society,2019:2477-2484. [9] CHOI J,CHUN D,KIM H,et al.Gaussian YOLOv3:An accurate and fast object detector using localization uncertainty for autonomous driving[J].arXiv:1904.04620v2,2019. [10] GIRSHICK R,DONAHUE J,DARRELL T,et al.Rich feature hierarchies for accurate object detection and semantic segmentation[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.IEEE Computer Society,2014:580-587. [11] UIJLINGS J R R,VAN DE SANDE K E A,GEVERS T,et al.Selective search for object recognition[J].International Journal of Computer Vision,2013,104(2):154-171. [12] HE K M,ZHANG X Y,REN S Q,et al.Spatial pyramid pooling in deep convolutional networks for visual Recognition[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,2015,37(9):1904-1916. [13] GIRSHICK,ROSS.Fast R-CNN[C]//IEEE International Conference on Computer vision.IEEE Computer Society,2015:1440-1448. [14] REN S,HE K,GIRSHICK R,et al.Faster R-CNN:Towards Real-Time Object Detection with Region Proposal Networks[J].IEEE Trans.Pattern Anal.Mach.Intell.,2017,39:1137-1149. [15] LIU W,ANGUELOV D.SSD:Single Shot MultiBox Detector[C]//The 14th European Conference on Computer Vision.2016:21-37. [16] SIMONYAN K,ZISSERMAN A.Very deep convolutional networks for large-scale image recognition[C]//3rd International Conference on Learning Representations.2015:1556. [17] HE K,ZHANG X,REN S,et al.Deep Residual Learning for Image Recognition[C]//IEEE Conference on Computer Vision and Pattern Recognition.IEEE Computer Society,2016:770-778. [18] LIN T Y,DOLLAR P,GIRSHICK R,et al.Feature pyramidnetworks for object detection[C]//IEEE Conference on Computer Vision and Pattern Recognition.IEEE Computer Society,2017:2117-2125. [19] HE K,GKIOXARI G,DOLLAR P,et al.Mask R-CNN[C]//IEEE international conference on computer vision.IEEE Computer Society,2017:2961-2969. [20] NEUBECK A,GOOL,Van L J.Efficient Non-Maximum Suppression[C]//International Conference on Pattern Recognition.IEEE Computer Society,2006:850-855. [21] STALLKAMP J,SCHLIPSING M,SALMEN J,et al.The germantraffic sign recognition benchmark:a multi-class classificationcompetition[C]//The International Joint Conference on Neural Networks.IEEE Computer Society,2011:1453-1460. [22] GEIGER A,LENZ P,URTASUN R.Are we ready for autonomous driving? The kitti vision benchmark suite [C]//IEEE Conference on Computer Vision and Pattern Recognition.IEEE Computer Society,2012:3354-3361. [23] REDMON J.Darknet:Open Source Neural Networks in C,2013-2016 [OL].http://pjreddie.com/darknet/. [24] LIN T Y,MAIRE M,BELONGIE S,et al.Microsoft COCO:Common objectsin context[C]//The 12th European Conference on Computer Vision.2014:740-755. |
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