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Hi, I have one question regarding to the lane evaluation method. My thought may be not correct, but please read it and give me some suggestions if possible, thank you.
In CULane, different images may have different lane end point labelling (eg. 280, 410 ...)
When extracting lane points from prob maps, the number of points determine the end point of lanes (eg. m=15 points result in the end points of lanes are 310)
Predicted lane (310) can be shorter than ground truth (280) ["estimation is not enough"] or longer than ground truth (410) ["over estimation"]
As the attached figure shown, I thought lane fitting is evaluated with almost same length, but here, different length of prediction and ground truth also affects on false positive and false negative
The text was updated successfully, but these errors were encountered:
@yoga-0125 Your understanding is correct. We believe that predicting the length accurately is also important for lane detection, thus length is considered in the evaluation metric.
Hi, I have one question regarding to the lane evaluation method. My thought may be not correct, but please read it and give me some suggestions if possible, thank you.
The text was updated successfully, but these errors were encountered: