L-YOLO:适用于车载边缘计算的实时交通标识检测模型

doi:10.11896/jsjkx.200800034

Abstract

Abstract: In the vehicle edge computing unit,due to the limited resources of its hardware equipment,it becomes more and more urgent to develop a lightweight and efficient traffic sign detection model for vehicle edge computing.This paper proposes a lightweight traffic sign detection model based on Tiny YOLO,which is called L-YOLO.Firstly,L-YOLO uses partial residual connection to enhance the learning ability of lightweight network.Secondly,in order to reduce the false detection and missed detection of traffic signs,L-YOLO uses Gauss loss function as the location loss of boundary box.In the traffic sign detection dataset named TAD16K,the parameter amount of L-YOLO is 18.8M,the calculation amount is 8.211BFlops,the detection speed is 83.3FPS,and the mAP reaches 86%.Experimental results show that the algorithm not only guarantees the real-time performance,but also improves the detection accuracy.

Key words: Vehicle edge computing, Object detection, Traffic sign detection, Convolutional neural network, Residual connection, Tiny YOLO

CLC Number:

TP391

SHAN Mei-jing, QIN Long-fei, ZHANG Hui-bing. L-YOLO:Real Time Traffic Sign Detection Model for Vehicle Edge Computing[J].Computer Science, 2021, 48(1): 89-95.

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L-YOLO:Real Time Traffic Sign Detection Model for Vehicle Edge Computing

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