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liuruijin17 avatar liuruijin17 commented on July 28, 2024

Sorry for being late, tough months about lots of deadlines.

Two methods:

  1. No training and directly use pre-trained models
    1-1 TuSimple
    Make sure the aspect ratio of your image is 360 to 640, then our test protocol resizes your input image to 360 x 640.
    1-2 CULane
    Make sure the aspect ratio of your image is 295 to 820, then our test protocol resizes your input image to 295 x 820.
    1-3 Why not good?
    Currently, images in TuSimple contain only highway scenes, and the quantity is small. Another CULane contains urban scenes a lot but still suffers the lack of lane shape pattern (curved, closely curved, remotely curved, ...).
    1-4 How to improve?
    Crop (or any other transformations) your images to make them have similar shape patterns like CULane (or TuSimple) since we treat each lane as a whole object.

  2. Annotating your image and training
    2-1. How to annotate? (Some recommendations)
    1). Annotate each lane with keypoints (10 ~ 20 points is ok, you can sparsely label the close part and densely label remote curved part for better performance on remote curved structures).
    2). Make sure the occluded part is correctly imaged when you annotate a lane
    3). Make sure the heavily occluded lane is labeled as ignored
    4). 2) and 3) are important to form consistent shape guidance, which avoids the problem of marker-appearance misalignment brought about by arbitrary markers.
    5) 2), 3) and 4) are found based on the fact that we treat each lane as a whole object (like a cat or dog in the object detection field) rather than low-level pixels.
    6) Remember and think 5), you will make your mind about the advantages and disadvantages of LSTR.
    2-2. Big gains
    1) Adding channels in the backbone, at least 4% CULane F1 improvement.
    2) Pre-train your model on a larger dataset, e.g. pre-trained on LLAMAS can bring at least 0.2% TuSimple accuracy improvement.

from lstr.

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