## Method

CLCFNet[la] [CLCFNet]

Submitted on 3 Nov. 2020 08:55 by
Shuo Gu (Nanjing University of Science and Technology)

 Running time: 0.02 s Environment: GPU @ 1.5 Ghz (Python)

 Method Description: LiDAR + Camera about 23ms per frame Parameters: TBA Latex Bibtex: @inproceedings{GuYK21, author = {Shuo Gu and Jian Yang and Hui Kong}, title = {A Cascaded LiDAR-Camera Fusion Network for Road Detection}, booktitle = {{ICRA}}, publisher = {{IEEE}}, year = {2021} }

## Evaluation in Bird's Eye View

 Benchmark MaxF AP PRE REC FPR FNR UM_ROAD 95.65 % 89.49 % 95.31 % 96.00 % 2.15 % 4.00 % UMM_ROAD 97.24 % 93.84 % 97.99 % 96.51 % 2.18 % 3.49 % UU_ROAD 95.68 % 88.37 % 94.75 % 96.63 % 1.75 % 3.37 % URBAN_ROAD 96.38 % 90.85 % 96.38 % 96.39 % 1.99 % 3.61 %
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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