UFLD enhancements: New loss function and image masking
Year: 2024
Type: article
Abstract: In the field of autonomous driving, lane detection plays a crucial role in ensuring safety and accuracy in navigation. This paper presents enhancements to the Ultra Fast Lane Detection (UFLD) model by introducing a new loss function that ensures smoother and more continuous lane lines, and incorporating image masking techniques to simulate real-world occlusions. These modifications improve the model’s robustness, particularly in scenarios with degraded lane markings. Through extensive experimentation on the TuSimple dataset, we demonstrate that the proposed enhancements lead to more accurate and realistic lane detection, especially in challenging environments.
Authors: Yanbing Chen, Qilong Wu, Xinyi Su
Institutions: Macau University of Science and Technology, Jinan University
Language: English
Subfield: Automotive Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W4403844725