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

Atrous Space Bender U-Net (ASBU-Net/LogiNet)