论文标题
语义分割，自动驾驶的多尺度空间关注
Semantic Segmentation With Multi Scale Spatial Attention For Self Driving Cars
论文作者
论文摘要
在本文中，我们在各种尺度上使用多量表特征融合提出了一个新颖的神经网络，以精确有效的语义图像分割。我们使用了基于重新安装的特征提取器，在下采样部分中的扩张卷积层，在上采样部分中使用的卷积层，并使用Concat操作将其合并。提出了一个新的注意模块，以编码更多的上下文信息并增强网络的接受场。我们通过培训和优化细节对网络进行了深入的理论分析。我们的网络在CAMVID数据集和CityScapes数据集上进行了培训和测试，使用每个类别的平均准确性和与Union（IOU）作为评估指标的相交。我们的模型在语义分割方面的先前状态优于先前的ART方法，即以> 100 fps的速度运行时达到74.12的含量为74.12。
In this paper, we present a novel neural network using multi scale feature fusion at various scales for accurate and efficient semantic image segmentation. We used ResNet based feature extractor, dilated convolutional layers in downsampling part, atrous convolutional layers in the upsampling part and used concat operation to merge them. A new attention module is proposed to encode more contextual information and enhance the receptive field of the network. We present an in depth theoretical analysis of our network with training and optimization details. Our network was trained and tested on the Camvid dataset and Cityscapes dataset using mean accuracy per class and Intersection Over Union (IOU) as the evaluation metrics. Our model outperforms previous state of the art methods on semantic segmentation achieving mean IOU value of 74.12 while running at >100 FPS.
