Code underlying the publication: Efficient Sequential Neural Network based on Spatial-Temporal Attention and Linear LSTM for Robust Lane Detection Using Multi-Frame Images
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This is the source code of the paper: Patil, S.#, Dong, Y.# ,*, Farah, H, & Hellendoorn, J. (2024). Efficient Sequential Neural Network Based on Spatial-Temporal Attention and Linear LSTM for Robust Lane Detection Using Multi-Frame Images (Under Review) How to use the codes (1) Download tvtLANE Dataset: You can download this **dataset** from the link in the '**Dataset-Description-v1.2.pdf**' file. BaiduYun：https://pan.baidu.com/s/1lE2CjuFa9OQwLIbi-OomTQ passcodes：tf9xOr Google Drive: https://drive.google.com/drive/folders/1MI5gMDspzuV44lfwzpK6PX0vKuOHUbb_?usp=sharing The **pretrained model** is also provided in the " /model " folder, named as 98.48263448061671_RAd_lr0.001_batch70_FocalLoss_poly_alpha0.25_gamma2.0_Attention_UNet_LSTM.pth . (2) Set up ## Requirements PyTorch 0.4.0 Python 3.9 CUDA 8.0 ## Preparation ### Data Preparation The dataset contains 19383 continuous driving scenes image sequences, and 39460 frames of them are labeled. The size of images is 128*256.The training set contains 19096 image sequences. Each 13th and 20th frame in a sequence are labeled, and the image and their labels are in “clips_13(_truth)” and “clips_20(_truth)”. All images are contained in …
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