Abstract:
In this final project, a system to actively detect potholes and speed breakers
using YOLOv3 as an object detection algorithm and its capability to provide
bounding box coordinates for each detected object as well as detect multiple object
classes simultaneously and Tensorflow 2 support makes it adaptable to various
applications with the goal of increasing awareness of road conditions in order to
improve road safety is developed. The existence of this system's development could
benefit users because it has made it simpler to identify potholes or speed breakers on
the road, which improves the safety of using the road using vehicles. This project was
created utilizing the Object detection frameworks and Computer Visions strategy,
which prioritizes real-time processing for object detection. This method is ideal for
this project because it was designed to make identifying objects easier and more
efficient for drivers in real-world circumstances. By combining advanced object
detection frameworks and computer vision techniques, mobile applications that
improve road safety for users can be developed. The device can instantaneously
recognize possible risks such as speed breakers and potholes, as well as deliver safety
alerts. The real-time processing capabilities ensure that drivers receive immediate
feedback, allowing them to react quickly to road conditions ahead.
