## Method

Resnet with GAN [SSLGAN]

Submitted on 9 Oct. 2017 01:32 by
Xiaofeng Han (School of computer science and engineering, Nanjing University of Science and Technology, China)

 Running time: 700ms Environment: GPU @ 1.5 Ghz (Python)

 Method Description: tba Parameters: tba Latex Bibtex: @article{Han2018Semisupervised, title={Semisupervised and Weakly Supervised Road Detection Based on Generative Adversarial Networks}, author={Han, Xiaofeng and Lu, Jianfeng and Zhao, Chunxia and You, Shaodi and Li, Hongdong}, journal={IEEE Signal Processing Letters}, volume={25}, number={4}, pages={551-555}, year={2018}, }

## Evaluation in Bird's Eye View

 Benchmark MaxF AP PRE REC FPR FNR UM_ROAD 94.62 % 89.50 % 95.32 % 93.93 % 2.10 % 6.07 % UMM_ROAD 96.72 % 92.99 % 97.05 % 96.40 % 3.22 % 3.60 % UU_ROAD 94.40 % 87.84 % 94.17 % 94.63 % 1.91 % 5.37 % URBAN_ROAD 95.53 % 90.35 % 95.84 % 95.24 % 2.28 % 4.76 %
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

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

## 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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