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Research map / Detection and segmentation / CNN features

Improved Road Connectivity by Joint Learning of Orientation and Segmentation

CVPR 2019 · Detection and segmentation › CNN features · approach: Lane detection · 240 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
jointly learn road orientation and segmentation (ablation)road extraction models · SpaceNetAPLS: 55.69 -> 60.77Helped
jointly learn road orientation and segmentation (ablation)road extraction models · DeepGlobeAPLS: 65.33 -> 68.71Helped
use junction prediction instead of orientation learning (ablation)ResNet18 road extraction · SpaceNetAPLS: 52.65 -> 52.76No clear effect
use two multi-branch stacks rather than one (ablation)orientation-and-segmentation model · SpaceNetAPLS: 60.92 -> 63.65Helped
increase the multi-branch model from two stacks to three (ablation)orientation-and-segmentation model · SpaceNetAPLS: 63.65 -> 62.89Hurt
use two multi-branch stacks rather than one (ablation)orientation-and-segmentation model · DeepGlobeAPLS: 70.23 -> 73.21Helped
increase the multi-branch model from two stacks to three (ablation)orientation-and-segmentation model · DeepGlobeAPLS: 73.21 -> 72.48Hurt
add summed cross-task feature fusion (ablation)two-stack multi-branch model · SpaceNetAPLS: 61.78 -> 63.65Helped
add connectivity refinement after segmentation (ablation)full road extraction pipeline · SpaceNet and DeepGlobeAPLSHelped

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