Abstract
This paper presents an algorithm for lane line detection based on convolutional neural network. The algorithm adopts the structural mode of encoder and decoder, in which the encoder part uses VGG16 combined with cavity convolution as the basic network to extract the features of lane lines, and the cavity convolution can expand the receptive field. Through experimental comparison, the full connection layer of the network is discarded, the last maximum pooling layer of the VGG16 network is removed, and the processing of the last three convolutional layers is replaced by empty convolution, which can better balance the detection rate and accuracy. The decoder part USES the index function of the maximum pooling layer to carry out up-sampling of the encoder in an anti-pooling way to achieve semantic segmentation, and combines with the instance segmentation, and finally realizes the detection of lane lines through fitting. The test results show that the algorithm has a good balance in speed and accuracy and good robustness.
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Acknowledgement
This work was supported by the industry-university-research innovation fund of science and technology development center of Ministry of Education: 2020QT02.
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Ding, L., Xu, Z., Zong, J., Xiao, J., Shu, C., Xu, B. (2021). A Lane Line Detection Algorithm Based on Convolutional Neural Network. In: Nguyen, M., Yan, W.Q., Ho, H. (eds) Geometry and Vision. ISGV 2021. Communications in Computer and Information Science, vol 1386. Springer, Cham. https://doi.org/10.1007/978-3-030-72073-5_14
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