Real-Time Detection of Objects on Roads for Autonomous Vehicles Using Deep Learning
Publication Date
1-1-2022
Document Type
Conference Proceeding
Publication Title
Proceedings - IEEE 8th International Conference on Big Data Computing Service and Applications, BigDataService 2022
DOI
10.1109/BigDataService55688.2022.00019
First Page
73
Last Page
80
Abstract
Autonomous driving has received much attention in academia and industries in recent years. Researchers incorporate different perceptions to detect vehicles, objects, routes, and lighting. However, few publications focus on road signs on pavement surface. This paper aims at creating a dataset with 10,000 images of U.S. road markings from similar datasets, web scraping, and raw driving footage in California. In addition, this paper proposes an ensemble deep learning model as a component to autonomous driving systems to allow real time detection of road markings and other objects. The proposed ensemble model is based on four modified models, including improved CenterNet, improved EfficientDet-D1, improved SSD and improved YOLOv4. Finally, the report experiments show that the proposed ensemble model classification of mobile objects on roads for autonomous vehicles.
Keywords
autonomous vehicles, computer vision, machine learning, road markings, traffic object detection
Department
Computer Engineering
Recommended Citation
Max Ng, Darshit Jagetiya, Xing Gao, Haoran Shi, Jerry Gao, and Jia Liu. "Real-Time Detection of Objects on Roads for Autonomous Vehicles Using Deep Learning" Proceedings - IEEE 8th International Conference on Big Data Computing Service and Applications, BigDataService 2022 (2022): 73-80. https://doi.org/10.1109/BigDataService55688.2022.00019
