基于改进YOLOv3算法的行人检测研究

doi:10.16180/j.cnki.issn1007-7820.2021.01.002

Abstract

Abstract:

The YOLOv3 algorithm uses Darknet53 as the backbone in the target detection (pedestrian detection) of a single object, and the network appears redundant, which results in too many parameters and slow detection speed. Additionally, the traditional bounding box loss function makes the detection and positioning inaccurate. To solve these problems, the improved YOLOv3 backbone network is proposed in the current study. A new multi-scale fusion network based on Darknet19 is constructed to accelerate the training speed and detection speed, and a generalized intersection over union loss function is introduced to improve the detection accuracy. The experimental results show that the proposed algorithm improves the accuracy of the original algorithm by 5% in the pedestrian detection dataset such as the INRIA pedestrian dataset. Compared with Faster R-CNN , the detection speed of a single image reaches 0.015 s per image under the condition of good accuracy.

Key words: target detection, generalized intersection over union, YOLOv3, multi-scale fusion, pedestrian detection, INRIA data set

CLC Number:

TN247

YE Fei, LIU Zilong. Pedestrian Detection Based on Improved YOLOv3 Algorithm[J].Electronic Science and Technology, 2021, 34(1): 5-9.

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参数	参数描述	值
Momentum	冲量常数	0.9
Decay	权重衰减系数	0.000 5
Batchsize	批量大小	64
L_r	学习率	0.000 1
Iteration	迭代次数	20 000

算法	精确率/%	召回率/%	每张检测时间 /s
Yolov3-Darnet19	70.23	82.20	0.014
Proposed method	73.12	83.45	0.015

算法	精确率/%	召回率/%	每张检测时间 /s
HOG+SVM	57.80	50.90	0.474
YOLOv3	68.13	78.42	0.051
Faster R-CNN	80.64	85.14	0.416
Proposed method	73.12	83.45	0.015

Pedestrian Detection Based on Improved YOLOv3 Algorithm

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