Real-time Large Scale Traffic Sign Detection

被引：0

作者：

Avramovic, Aleksej ^{[1
]}

Tabernik, Domen ^{[2
]}

Skocaj, Danijel ^{[2
]}

机构：

[1] Univ Banja Luka, Fac Elect Engn, Patre 5, Banja Luka 78000, Bosnia & Herceg

[2] Univ Ljubljana, Fac Comp & Informat Sci, Vecna Pot 113, SI-1000 Ljubljana, Slovenia

来源：

2018 14TH SYMPOSIUM ON NEURAL NETWORKS AND APPLICATIONS (NEUREL) | 2018年

关键词：

Deep learning; traffic sign detection; real-time; traffic sign recognition; YOLO;

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Automatic traffic sign detection and recognition has achieved good results using convolutional neural networks. Novel architectures are still being proposed in order to improve accuracy of detection and segmentation of traffic sings. In this paper, we are examining the possibility for traffic sign detection and recognition in real-time. For that purpose, we employed a novel YOLOv3 architecture, which has been proven to be fast and accurate method for object detection. It was shown that real-time detection can be achieved, even on HD images, with mAP above 88%.

引用

页数：4

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