Towards a safe and comfortable driving, road scene segmentation is a
rudimentary problem in camera-based advance driver assistance systems (ADAS).
Despite of the great achievement of Convolutional Neural Networks (CNN) for
semantic segmentation task, the high computational efforts of CNN based methods
is still a challenging area. In recent work, we proposed a novel approach to
utilise the advantages of CNNs for the task of road segmentation at reasonable
computational effort. The runtime benefits from using irregular super pixels as
basis for the input for the CNN rather than the image grid, which tremendously
reduces the input size. Although, this method achieved remarkable low
computational time in both training and testing phases, the lower resolution of
the super pixel domain yields naturally lower accuracy compared to high cost
state of the art methods. In this work, we focus on a refinement of the road
segmentation utilising a Conditional Random Field (CRF).The refinement
procedure is limited to the super pixels touching the predicted road boundary
to keep the additional computational effort low. Reducing the input to the
super pixel domain allows the CNNs structure to stay small and efficient to
compute while keeping the advantage of convolutional layers and makes them
eligible for ADAS. Applying CRF compensate the trade off between accuracy and
computational efficiency. The proposed system obtained comparable performance
among the top performing algorithms on the KITTI road benchmark and its fast
inference makes it particularly suitable for realtime applications.

本文提出了一种使用 CNN 和 CRF 相结合的方法进行道路场景分割，采用超像素为 CNN 的输入基础，使计算效率更高，在保持较快运行速度的同时，在 KITTI 路标准测试中取得了可比拟的性能，适合于实时应用

使用基于超像素的 CNN 和 CRF 模型实现高效细粒度道路分割

Efficient fine-grained road segmentation using superpixel-based CNN and CRF models

Detection of partially occluded objects is a challenging computer vision
problem. Standard Convolutional Neural Network (CNN) detectors fail if parts of
the detection window are occluded, since not every sub-part of the window is
discriminative on its own. To address this issue, we propose a novel loss layer
for CNNs, named grid loss, which minimizes the error rate on sub-blocks of a
convolution layer independently rather than over the whole feature map. This
results in parts being more discriminative on their own, enabling the detector
to recover if the detection window is partially occluded. By mapping our loss
layer back to a regular fully connected layer, no additional computational cost
is incurred at runtime compared to standard CNNs. We demonstrate our method for
face detection on several public face detection benchmarks and show that our
method outperforms regular CNNs, is suitable for realtime applications and
achieves state-of-the-art performance.

本研究提出了一种名为 Grid loss 的 CNN 模型损失函数，该函数能够独立地在卷积层的子块上最小化误差率，以实现部分遮挡物体的检测，经实证表明，该方法可以用于人脸检测，并在几个公共数据集上跑出了最优性能，适用于实时应用程序。