Semantic Segmentation With Multi Scale Spatial Attention For Self Driving Cars

In this paper, we present an architecture using multi scale feature fusion at various scales for accurate and efficient semantic image segmentation. We have used dilated convolutional layers in downsampling layers and transposed convolutional layers in the upsampling layers and used concat layers to merge them. We have used skip connections in between alternate blocks which are comprised of convolutional and max pooling layers. We present an in depth theoretical analysis of our network with training and optimization details. We evaluated our network on the Camvid dataset using mean accuracy per class and Intersection Over Union (IOU) as the evaluation metrics on the test set. Our model outperforms previous state of the art on semantic segmentation achieving mean IOU value of 74.12 while running at >100 FPS.