Sitti Mawaddah Umar

doi:10.55338/jumin.v6i3.6052

Authors

Sitti Mawaddah Umar Politeknik Kesehatan Megarezky
Universitas Mega Buana Palopo

DOI:

https://doi.org/10.55338/jumin.v6i3.6052

Abstract

This study aims to detect traffic violations, specifically motorcycles riding against the flow on one-way roads, by utilizing computer vision technology to recognize the rearview patterns of vehicles. The method employed involves applying the deep learning model Faster-RCNN for object detection, using image data captured from an IP camera mounted on a pole at a height of 2.5 meters with a 45-degree tilt angle to optimally monitor vehicles from behind. Image labeling was performed using the LabelImg application, while model training and classification were conducted using the TensorFlow framework. The developed system achieved a detection accuracy of 88%, demonstrating the effectiveness of this approach in identifying motorcycles violating traffic direction. These findings highlight the potential of computer vision as an automatic and real-time solution for traffic monitoring, which can help reduce dangerous violations and enhance road safety. Therefore, this research contributes significantly to the development of more advanced and efficient traffic violation detection systems.

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