Binary DAD-Net：用于自动驾驶的二值化可行驶区域检测网络
Binary DAD-Net: Binarized Driveable Area Detection Network for Autonomous Driving
摘要：
可行驶区域检测是自动驾驶（AD）领域中各种应用的关键组成部分，例如地平面检测、障碍物检测和机动规划。此外，庞大和过度参数化的网络可以很容易地放弃，并替换为更小的网络，以便在嵌入式系统上进行更快的推理。可行驶区域检测，提出了两类分割任务，可以有效地建模与超薄二进制网络。本文提出了一种新的二进制可驱动区域检测网络（二进制DAD-Net），它只使用二进制的权重和激活的编码器，瓶颈和解码器部分。瓶颈的潜在空间通过二进制扩张卷积有效地增加（x32 -> x16下采样），学习更复杂的特征。与自动生成的训练数据一起，二进制DAD-Net在公共数据集上的性能优于最先进的语义分割网络。与全精度模型相比，我们的方法在FPGA上的计算复杂度降低了14.3倍，并且只需要0.9MB的内存资源。因此，基于SIMD的AD硬件能够加速二进制DAD网络。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
