Akademik Veri Yönetim Sistemi
7th INTERNATIONAL CONGRESS ON ENGINEERING AND TECHNOLOGY MANAGEMENT, İstanbul, Türkiye, 16 - 18 Nisan 2022, ss.361-367
Today, most of the urban and
extra-urban transportation services are provided by highways. Potholes on the
highway significantly affect driver safety by reducing road quality. The
potholes on the highway cause serious traffic accidents as well as financially
damage the vehicles on the highway. In order to increase driver safety and
reduce traffic accidents, a driver assistance system that detects potholes on
the highway surface should be integrated into vehicles. The realization of a
system that detects potholes on the highway and gives a pre-warning for driver
assistance systems will prevent material damage to highway vehicles and reduce
traffic accidents. For this reason, the development and application of deep
learning algorithms, which have been used in different object detections and
have shown great success in real-life problems in recent years, make it
possible to detect highway potholes. In this study, a system that detects
potholes on the highway is proposed using Yolo-v4, one of the deep learning algorithms.
In order to realize the proposed system, a dataset consisting of a total of 681
highway images, including 329 highway potholes images and 352 normal highway
images, was used. Using this data set, the model training and testing of the
proposed system and the performance test used in real-life problems were
carried out. According to the experimental results, it was seen that potholes
on the highway were detected quickly, reliably, and successfully using the
Yolo-v4 deep learning algorithm.