INTEGRATED TRAFFIC VIOLATION TYPE DETECTION AND RECOGNITION SYSTEM USING VIDEO PROCESSING BASED CONVOLUTIONAL NEURAL NETWORK
Penulis/Author
Ika Candradewi, S.Si., M.Cs. (1); ILHAM FAZRI (2)
Tanggal/Date
23 2023
Kata Kunci/Keyword
Abstrak/Abstract
Based on data from the World Health Organization in 2018, the number of
deaths worldwide due to road traffic accidents is 1.35 million people every year. One of
the causes is the low level of driver discipline in driving, which is indicated by violating traffic regulations. The solution for this problem is implementing a traffic violation
detection system based on computer vision and deep learning. The system designed in
this study can detect traffic violations that include running a red light, not wearing a
helmet, and being wrong-way. This study implements the YOLOv5 architecture as object
detection has 74% mAP50 value performance and uses SORT as object tracking. Vehicle detectors and trackers are then integrated with methods designed to detect traffic
violations. The test results using several sample video scenarios show that the running
red light detector has an F1-Score value of 0.92. The helmet violation detector based on
EfficientNet as a classifier has an F1-Score value of 0.88, and the wrong way detector
has an F1-Score value of 1.00. This research repository can be accessed at the following
