Abstract
In recent years, many lane detection methods have been proposed. However, most of them lead to unsatisfactory performance in handling some extreme difficult driving scenes such as shadows, wireless and dark night. Aiming at this problem, a multi-frame lane detection method based on UNET_CLB was proposed. This method introduced multi-frame information of continuous driving scenes for lane detection on the basis of traditional deep learning. Convolutional neural network (CNN) is combined with convolutional long short-term memory network (CONVLSTM) and deep densely connected convolutional networks (DENSE_NET), a deep advanced semantic extraction network was proposed. The experimental results on the public datasets show that this method achieves an F1-score of 92.391% on the TuSimple dataset, and the F1-score on the CULane dataset is up to 13.6% higher than the existing method. The simulation results based on the Webots platform also show that the method proposed in this paper has a good effect on lane detection in wireless, shadow and shadow environments.
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Liu, J., Gao, Y. (2022). A Multi-frame Lane Detection Method Based on Deep Learning. In: Sun, F., Hu, D., Wermter, S., Yang, L., Liu, H., Fang, B. (eds) Cognitive Systems and Information Processing. ICCSIP 2021. Communications in Computer and Information Science, vol 1515. Springer, Singapore. https://doi.org/10.1007/978-981-16-9247-5_19
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DOI: https://doi.org/10.1007/978-981-16-9247-5_19
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