|Table of Contents|

Detection of taxi driver's mask wearing based on improved YOLOv3 algorithm(PDF)

长安大学学报（自然科学版）[ISSN:1006-6977/CN:61-1281/TN]

Issue:
2023年6期
Page:
106-115
Research Field:
交通工程
Publishing date:

Info

Title:
Detection of taxi driver's mask wearing based on improved YOLOv3 algorithm
Author(s):
SUN Yong1;  WEI Ze-fa1;  CUI Hua2;  SONG Huan-sheng3
(1. Educational Technology and Network Center, Chang'an University, Xi'an 710064, Shaanxi, China; 2. College of Future Transportation, Chang'an University, Xi'an 710064, Shaanxi, China; 3. School of Information Engineering, Chang'an University, Xi'an 710064, Shaanxi, China)
Keywords:
traffic engineering;  image processing;  depth separable convolution;  multi scale prediction;  mask detection
PACS:
U491.3
DOI:
10.19721/j.cnki.1671-8879.2023.06.010
Abstract:
In order to monitor the mask wearing of taxi drivers in working state more timely and accurately, a mask wearing detection method for taxi drivers based on YOLOv3 improved algorithm was proposed. Before the beginning of the experiment, using the video data collected from the previous work, a dataset containing 2 478 images of taxi drivers working inside was created. According to the characteristics of the dataset, three improvement strategies were adopted. First of all, the ordinary convolution in the main network was replaced by the deep separable convolution, the model compression and the increase of network structure depth were realized while reducing the number of model parameters. Then, in order to ensure the fusion effect of mask edge information in the multi-scale prediction process, the original three feature images was reduced to two in the multi-scale fusion. Finally, in order to maintain the number of anchor frames in the feature image fusion process, K-means algorithm was used to recalculate 8 initial anchor frame values, and four were allocated on each fusion feature image, through the above improvements, the algorithm can better adapt to self-built datasets. The results show that the accuracy of the improved YOLOV3 algorithm can be improved to 96.2%, and the model is compressed to 32 M. The processing speed is 43 frames per second in NVIDIA 1080Ti environment, which meets the real-time requirements. Therefore, it can be seen that the improved algorithm performs better and can be effectively used for mask wearing detection of taxi drivers.2 tabs, 10 figs, 23 refs.

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Last Update: 2023-10-30