  $ $With recent advances in CNNs, exceptional improvements have been made in
semantic segmentation of high resolution images in terms of accuracy and
latency. However, challenges still remain in detecting objects in crowded
scenes, large scale variations, partial occlusion, and distortions, while still
maintaining mobility and latency. We introduce a fast and efficient
convolutional neural network, ASBU-Net, for semantic segmentation of high
resolution images that addresses these problems and uses no novelty layers for
ease of quantization and embedded hardware support. ASBU-Net is based on a new
feature extraction module, atrous space bender layer (ASBL), which is efficient
in terms of computation and memory. The ASB layers form a building block that
is used to make ASBNet. Since this network does not use any special layers it
can be easily implemented, quantized and deployed on FPGAs and other hardware
with limited memory. We present experiments on resource and accuracy trade-offs
and show strong performance compared to other popular models.
