Objective: This article aims to upgrade the lane detection algorithm from image to video level in order to advance automatic driving technology. The objective is to propose a cost-efficient algorithm that can handle complex traffic scenes and different driving speeds using continuous image inputs.
Methods: To achieve this objective, we introduce the Multi-ERFNet-ConvLSTM network framework, which combines Efficient Residual Factorized ConvNet (ERFNet) and Convolution Long Short Term Memory (ConvLSTM). Additionally, we incorporate the Pyramidally Attended Feature Extraction (PAFE) Module into our network design to effectively handle multi-scale lane objects. The algorithm is evaluated using a divided dataset and comprehensive assessments are conducted across multiple dimensions.
Results: In the testing phase, the Multi-ERFNet-ConvLSTM algorithm surpasses the primary baselines and demonstrates superior performance in terms of Accuracy, Precision, and F1-score metrics. It exhibits excellent detection results in various complex traffic scenes and performs well at different driving speeds.
Conclusions: The proposed Multi-ERFNet-ConvLSTM algorithm provides a robust solution for video-level lane detection in advanced automatic driving. By utilizing continuous image inputs and incorporating the PAFE Module, the algorithm achieves high performance while reducing labeling costs. Its exceptional accuracy, precision, and F1-score metrics highlight its effectiveness in complex traffic scenarios. Moreover, its adaptability to different driving speeds makes it suitable for real-world applications in autonomous driving systems.
Keywords: CNN-RNN-based network; Continuous traffic scenes; deep learning; lane detection.