GitHub - megk96/multispectral-maskrcnn: The goal of this project is to ascertain the role of intensity and depth channels in instance segmentation of traffic signs. It has been shown that secondary channels, like intensity and depth, provide complementary information to the neural net and can thus boost the efficiency of a model. The project aims to explore the ramifications of experimenting with intensity or reflectivity and depth channels along with RGB images and determine the optimal architecture to combine them most effectively. The intensity and depth data is obtained from LIDAR sensors. Two different implementations of substituting channels in Mask R-CNN are explored.

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The goal of this project is to ascertain the role of intensity and depth channels in instance segmentation of traffic signs. It has been shown that secondary channels, like intensity and depth, provide complementary information to the neural net and can thus boost the efficiency of a model. The project aims to explore the ramifications of experi…

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Name		Name	Last commit message	Last commit date
Latest commit History 6 Commits
alplab.py		alplab.py
cityscapes.py		cityscapes.py
cityscapes_create_tfrecords.py		cityscapes_create_tfrecords.py
create_a9_lassnitzhoehe_from_shape_tsl_tf_record.py		create_a9_lassnitzhoehe_from_shape_tsl_tf_record.py
depth-intensity.py		depth-intensity.py
final-mask-lines.py		final-mask-lines.py
mask-lines.py		mask-lines.py
project_shp_to_instance_image.py		project_shp_to_instance_image.py
shapefile-projection.py		shapefile-projection.py
shp_rgbdi.py		shp_rgbdi.py
signs_from_shpfile.py		signs_from_shpfile.py
warp-image.py		warp-image.py