Method

FCN - Training with Large Patches [FTP]


Submitted on 11 Jan. 2016 14:24 by
Ankit Laddha (Carnegie Mellon University)

Running time:0.28 s
Environment:GPU @ 2.5 Ghz (C/C++)

Method Description:
Finetuning Deeplab FCN pretrained on Imagenet
Parameters:
-
Latex Bibtex:
@INPROCEEDINGS{Laddha2016IV,
author={Ankit Laddha and Mehmet Kemal Kocamaz and Luis E. Navarro-Serment and Martial Hebert},
booktitle={IEEE Intelligent Vehicles Symposium Proceedings},
title={Map-Supervised Road Detection},
year={2016},
}

Evaluation in Bird's Eye View


Benchmark MaxF AP PRE REC FPR FNR
UM_ROAD 91.20 % 90.60 % 91.11 % 91.29 % 4.06 % 8.71 %
UMM_ROAD 92.98 % 92.89 % 91.84 % 94.15 % 9.20 % 5.85 %
UU_ROAD 89.62 % 88.93 % 89.10 % 90.14 % 3.59 % 9.86 %
URBAN_ROAD 91.61 % 90.96 % 91.04 % 92.20 % 5.00 % 7.80 %
This table as LaTeX

Behavior Evaluation


Benchmark PRE-20 F1-20 HR-20 PRE-30 F1-30 HR-30 PRE-40 F1-40 HR-40
This table as LaTeX

Road/Lane Detection

The following plots show precision/recall curves for the bird's eye view evaluation.



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Distance-dependent Behavior Evaluation

The following plots show the F1 score/Precision/Hitrate with respect to the longitudinal distance which has been used for evaluation.


Visualization of Results

The following images illustrate the performance of the method qualitatively on a couple of test images. We first show results in the perspective image, followed by evaluation in bird's eye view. Here, red denotes false negatives, blue areas correspond to false positives and green represents true positives.



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