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

arXiv: https://arxiv.org/abs/2006.08178 · HTML: https://mlsanity.com/paper/2006.08178/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Jun 2020.

## Built on by (most cited first, 10 of 10)

- [A comprehensive review of Binary Neural Network](https://mlsanity.com/paper/2110.06804/) (2021)
- [BinaryCoP: Binary Neural Network-based COVID-19 Face-Mask Wear and Positioning Predictor on Edge Devices](https://mlsanity.com/paper/2102.03456/) (2021)
- [Pathfinder for Low-altitude Aircraft with Binary Neural Network](https://mlsanity.com/paper/2409.08824/) (2024)
- [Visual Domain Adaptation for Monocular Depth Estimation on Resource-Constrained Hardware](https://mlsanity.com/paper/2108.02671/) (2021)
- [You Only Look Bottom-Up for Monocular 3D Object Detection](https://mlsanity.com/paper/2401.15319/) (2024)
- [BOLD: Boolean Logic Deep Learning](https://mlsanity.com/paper/2405.16339/) (2024)
- [The Binary Quantized Neural Network for Dense Prediction via Specially Designed Upsampling and Attention](https://mlsanity.com/paper/2405.17776/) (2024)
- [L2PF -- Learning to Prune Faster](https://mlsanity.com/paper/2101.02663/) (2021)
- [Information-Bottleneck Driven Binary Neural Network for Change Detection](https://mlsanity.com/paper/2507.03504/) (2025)
- [Learning 1-Bit LiDAR-based Localization with Auxiliary Objective](https://mlsanity.com/paper/2606.27729/) (2026)

## Abstract (arXiv)

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.