Performance comparison of deep learning based lane detection models for autonomous vehicles

Autonomous driving systems are recognized as one of the most significant advancements in modern transportation technologies. To ensure the safe and efficient operation of these systems, it is crucial that they excel in critical tasks, particularly environmental perception and lane detection. This thesis study addresses deep learning-based approaches for lane detection in autonomous driving systems. The primary objective of this thesis is to comparatively analyze the lane detection performance of four different deep learning architectures: U-Net, SCNN, ENet, and ENet-SAD. For this purpose, both the widely used TuSimple dataset and an originally created dataset were utilized. The models were evaluated using various performance metrics such as accuracy, precision, recall, F1-score, and IoU. Additionally, a qualitative analysis was conducted on the visual outputs of the models. The results revealed that the SCNN model generally demonstrated the highest performance on both datasets. SCNN particularly stood out with its ability to accurately detect lanes under complex road structures and variable conditions. However, SCNN's relatively high processing time can be considered a disadvantage for real-time applications. Although the U-Net model had a lower accuracy rate compared to SCNN, it offered a significantly faster inference time. This feature makes U-Net a suitable candidate for real-time applications. ENet and ENet-SAD models showed lower performance compared to the other two models. However, their lighter structure and lower computational power requirements make them suitable for use in resource-constrained environments. The results obtained on the original dataset showed great consistency with the results on the TuSimple dataset, demonstrating that the models' performance can be generalized across different datasets. This thesis study is expected to contribute to the development of autonomous driving technology and research in the field of lane detection.