YOLO-Based Enhancement of Public Safety on Roads and Transportation in Bangladesh

Abstract

In order to upgrade the efficiency level of
multiple tracking like face, actions, characters, a deep
learning method is introduced to reduce the accidents
occurred in roads for carelessness and also to capture
the criminals in Bangladesh. This paper presents a faster
processing multiple detection method with the best
possible outcome under the framework of YOLOv2
algorithm in the event of car accident, crossing foot over
bridge and using the zebra crossing in Bangladesh.
Different layers are added to the YOLOv2 algorithm to
pass the information in various convolutional layers to
detect multiple objects with actions. In this paper
YOLOv2 algorithm under DarkFlow framework is used
to achieve higher ratio of confidence value as the max
convolutional layers reorganize the feature map so that
other layers feature map can be matched with the
bottom layers to achieve the expected output of the
indicated events. By removing the noise from the
unrelated area, the detections of the training video and
test video adopt quite parallel confidence ratio.