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This work aims to advance semantic segmentation for autonomous vehicles by utilizing a novel RGB-Thermal dataset.

September 1, 2017

MFNet: Towards real-time semantic segmentation for autonomous vehicles with multi-spectral scenes

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This work aims to advance semantic segmentation for autonomous vehicles by utilizing a novel RGB-Thermal dataset.
Developed a new convolutional neural network architecture tailored for multi-spectral image segmentation.
Introduced a dataset combining thermal and RGB images to benchmark performance.
Focused on maintaining segmentation accuracy during real-time processing.
Significant increase in segmentation accuracy observed when adding thermal infrared information.
Achieved improved performance in poor visibility conditions, notably at night and during adverse weather.

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Randomized trial demonstrates improved segmentation accuracy in self-driving vehicles, suggesting enhanced visibility in challenging conditions.

Ha et al. (2017) studied this question.

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Randomized trial demonstrates improved segmentation accuracy in self-driving vehicles, suggesting enhanced visibility in challenging conditions.