@Article{jai.2020.010137,
AUTHOR = {Yanming Wang, Kebin Jia, Pengyu Liu},
TITLE = {Impolite Pedestrian Detection by Using Enhanced YOLOv3-Tiny},
JOURNAL = {Journal on Artificial Intelligence},
VOLUME = {2},
YEAR = {2020},
NUMBER = {3},
PAGES = {113--124},
URL = {http://www.techscience.com/jai/v2n3/39519},
ISSN = {2579-003X},
ABSTRACT = {In recent years, the problem of “Impolite Pedestrian” in front of the zebra 
crossing has aroused widespread concern from all walks of life. The traffic sector’s 
governance measures have become more serious. The traditional way of governance is onsite law enforcement, which requires a lot of manpower and material resources and is low 
efficiency. An enhanced YOLOv3-tiny model is proposed for pedestrians and vehicle 
detection in traffic monitoring. By modifying the backbone network structure of YOLOv3-
tiny model, introducing deep detachable convolution operation, and designing the basic 
residual block unit of the network, the feature extraction ability of the backbone network 
is enhanced. The improved model is trained on the VOC2007+VOC2012 training set, and 
the trained model is tested for performance on the test data set. The experimental results 
show that: the mean Average Precision (mAP) increased from 0.672 to 0.732, increasing 
the measurement accuracy by 9%. The Intersection over Union (IoU) increased from 0.783 
to 0.855, increasing the coverage accuracy by 7.2%. The enhanced YOLOv3-tiny model 
has higher measurement accuracy than the original model. Applying this model to the 
1080P traffic video on the NVIDIA RTX 2080, the detection speed is 150 FPS, which can 
fully achieve real-time detection. Through the analysis of pedestrians and vehicle 
coordinates, it is judged whether or not illegal acts occur. For illegal vehicles, save three 
pictures as the basis for law enforcement, which forms an important supplement to off-site 
law enforcement.},
DOI = {10.32604/jai.2020.010137}
}