Binary DAD-Net: Binarized Driveable Area Detection Network for Autonomous Driving

Alexander Frickenstein; Manoj Rohit Vemparala; Jakob Mayr; Naveen Shankar Nagaraja; Christian Unger; Federico Tombari; Walter Stechele

arXiv · 2006.08178

Binary DAD-Net: Binarized Driveable Area Detection Network for Autonomous Driving

Alexander Frickenstein·Manoj Rohit Vemparala·Jakob Mayr·Naveen Shankar Nagaraja·Christian Unger·Federico Tombari·Walter Stechele

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Abstract

Driveable area detection is a key component for various applications in the field of autonomous driving (AD), such as ground-plane detection, obstacle detection and maneuver planning. Additionally, bulky and over-parameterized networks can be easily forgone and replaced with smaller networks for faster inference on embedded systems. The driveable area detection, posed as a two class segmentation task, can be efficiently modeled with slim binary networks. This paper proposes a novel binarized driveable area detection network (binary DAD-Net), which uses only binary weights and activations in the encoder, the bottleneck, and the decoder part. The latent space of the bottleneck is efficiently increased (x32 -> x16 downsampling) through binary dilated convolutions, learning more complex features. Along with automatically generated training data, the binary DAD-Net outperforms state-of-the-art semantic segmentation networks on public datasets. In comparison to a full-precision model, our approach has a x14.3 reduced compute complexity on an FPGA and it requires only 0.9MB memory resources. Therefore, commodity SIMD-based AD-hardware is capable of accelerating the binary DAD-Net.

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Alexander Frickenstein, Manoj Rohit Vemparala, Jakob Mayr, Naveen Shankar Nagaraja, Christian Unger, Federico Tombari, Walter Stechele. 2020-06-15. Binary DAD-Net: Binarized Driveable Area Detection Network for Autonomous Driving. https://arxiv.org/abs/2006.08178

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