Abstract
This paper addresses the problem of ego and adjacent lanes detection for real-life autonomous driving. Lane detection is a central part in vehicle automation applications, requiring accurate and stable detection in diverse difficult situations with heavy obstructions, erased lane markings or night. To overcome these challenges, we propose a predictive instance segmentation CNN model named S-UNet, combining spatial layers and U-Net style architecture to efficiently detect and infer lines formed by lane markings from non-corrected lateral fisheye cameras. Experimental results show that S-UNet is able to learn the spatial relationships and the continuous prior of lanes and achieves high accuracy and robustness under various conditions in real-life autonomous driving scenarios. It outperforms state-of-the-art model SCNN [17] by \(+\)5.57% mean IoU with significant improvement of the line marking detection accuracy.
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Moujtahid, S., Benmokhtar, R., Breheret, A., Boukhdhir, SE. (2022). Spatial-UNet: Deep Learning-Based Lane Detection Using Fisheye Cameras for Autonomous Driving. In: Sclaroff, S., Distante, C., Leo, M., Farinella, G.M., Tombari, F. (eds) Image Analysis and Processing – ICIAP 2022. ICIAP 2022. Lecture Notes in Computer Science, vol 13232. Springer, Cham. https://doi.org/10.1007/978-3-031-06430-2_48
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