Code underlying the publication: Robust Lane Detection Through Self Pre-Training with Masked Sequential Autoencoders and Fine-Tuning With Customized PolyLoss
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This is the code implementation for the paper:R. Li and Y. Dong, "Robust Lane Detection Through Self Pre-Training With Masked Sequential Autoencoders and Fine-Tuning With Customized PolyLoss," in IEEE Transactions on Intelligent Transportation Systems , vol. 24, no. 12, pp. 14121-14132, Dec. 2023, doi: 10.1109/TITS.2023.3305015. # Network Architecture In this study, three neural network models, i.e., UNet_ConvLSTM, SCNN_UNet_ConvLSTM, and SCNN_UNet_Attention are tested. # tvtLANE Dataset ## Description: (adapted from https://github.com/qinnzou/Robust-Lane-Detection)This dataset contains 19383 image sequences for lane detection, and 39460 frames of them are labeled. These images were divided into two parts, a training dataset contains 9548 labeled images and is augmented by four times, and a test dataset has 1268 labeled images. The size of images in this dataset is 128*256 pixels.+ Training set: - Data augmentation:The training set is augmented. By flipping and rotating the images at three degrees, the data volume is quadrupled. These augmented data are separated from the original training set, which is named by “origin”. “f” and “3d” after “-” are represented for …
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