Electronic Science and Technology ›› 2021, Vol. 34 ›› Issue (1): 5-9.doi: 10.16180/j.cnki.issn1007-7820.2021.01.002

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Pedestrian Detection Based on Improved YOLOv3 Algorithm

YE Fei,LIU Zilong   

  1. School of Optical-Electrical and Computer Engineering,University of Shanghai for Science and Technology,Shanghai 200093,China
  • Received:2019-10-15 Online:2021-01-15 Published:2021-01-22
  • Supported by:
    ational Natural Science Foundation of China(61603255)


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