Analysis of FCN8 and YOLOv8 in Enhancing Road Segmentation for Autonomous Driving
Keywords:
Deep Learning, Computer Vision, Semantic Segmentation, Fully Convolutional Neural Networks, Yolov8.Abstract
In the rapidly advancing field of autonomous driving, achieving precise road segmentation is paramount for accurate environmental perception. This research paper introduces an innovative road segmentation approach, which leverages the FCN-8 architecture with VGG16 as its backbone, further enhanced through the implementation of the sigmoid activation function and the Adam optimiser. In addition, we conduct a comprehensive comparative analysis with the YOLOv8 architecture to provide a holistic view of road segmentation solutions in this context. This research is based on a custom dataset specifically curated to address the unique challenges of autonomous driving scenarios, ensuring that the models are tested against real-world conditions. Our evaluation demonstrates the robustness and efficiency of both approaches in the context of autonomous driving, shedding light on their respective strengths and weaknesses. This innovative methodology not only advances road segmentation accuracy but also significantly contributes to the realm of self-driving technology, fostering safer and more reliable autonomous vehicles in real-world applications.
DOI: https://doi.org/10.24321/3051.4266.202602
References
T. H. A. Lau, "Road Segmentation with Neural Networks," Scholarly Horizons: University of Minnesota, Morris Undergraduate Journal, vol. 5, no. 2, Article 5, 2018.
A. Kebir, M. Taibi, and F. Serradilla, "Compressed VGG16 Auto-Encoder for Road Segmentation from Aerial Images with Few Data Training," in Proceedings of the 2nd International Conference on Complex Systems and Their Applications (ICCSA'2021), 2022.
J. Long, E. Shelhamer, and T. Darrell, "Fully convolutional networks for semantic segmentation," in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2015, pp. 3431-3440.
J. Niemeijer, P. Pekezou Fouopi, S. Knake-Langhorst, and E. Barth, "A Review of Neural Network based Semantic Segmentation for Scene Understanding in Context of the Self-Driving Car," 2023.
