Citation

Dewi C, Chen RC, Zhuang YC, Jiang X, Yu H. Big Data Cogn. Comput. 2023; 7(1): e54.

Copyright

(Copyright © 2023, MDPI: Multidisciplinary Digital Publications Institute)

DOI

10.3390/bdcc7010054

PMID

unavailable

Abstract

In recent years, there have been significant advances in deep learning and road marking recognition due to machine learning and artificial intelligence. Despite significant progress, it often relies heavily on unrepresentative datasets and limited situations. Drivers and advanced driver assistance systems rely on road markings to help them better understand their environment on the street. Road markings are signs and texts painted on the road surface, including directional arrows, pedestrian crossings, speed limit signs, zebra crossings, and other equivalent signs and texts. Pavement markings are also known as road markings. Our experiments briefly discuss convolutional neural network (CNN)-based object detection algorithms, specifically for Yolo V2, Yolo V3, Yolo V4, and Yolo V4-tiny. In our experiments, we built the Taiwan Road Marking Sign Dataset (TRMSD) and made it a public dataset so other researchers could use it. Further, we train the model to distinguish left and right objects into separate classes. Furthermore, Yolo V4 and Yolo V4-tiny results can benefit from the "No Flip" setting. In our case, we want the model to distinguish left and right objects into separate classes. The best model in the experiment is Yolo V4 (No Flip), with a test accuracy of 95.43% and an IoU of 66.12%. In this study, Yolo V4 (without flipping) outperforms state-of-the-art schemes, achieving 81.22% training accuracy and 95.34% testing accuracy on the TRMSD dataset.

Language: en

Keywords

CNN; machine learning; object recognition; road sign; Yolo