{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/segnet-a-deep-convolutional-encoder-decoder","title":"SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation","arxiv_id":"1511.00561","date":"2015-11-02","proceeding":null,"authors":["Vijay Badrinarayanan","Alex Kendall","Roberto Cipolla"],"abstract":"We present a novel and practical deep fully convolutional neural network\narchitecture for semantic pixel-wise segmentation termed SegNet. This core\ntrainable segmentation engine consists of an encoder network, a corresponding\ndecoder network followed by a pixel-wise classification layer. The architecture\nof the encoder network is topologically identical to the 13 convolutional\nlayers in the VGG16 network. The role of the decoder network is to map the low\nresolution encoder feature maps to full input resolution feature maps for\npixel-wise classification. The novelty of SegNet lies is in the manner in which\nthe decoder upsamples its lower resolution input feature map(s). Specifically,\nthe decoder uses pooling indices computed in the max-pooling step of the\ncorresponding encoder to perform non-linear upsampling. This eliminates the\nneed for learning to upsample. The upsampled maps are sparse and are then\nconvolved with trainable filters to produce dense feature maps. We compare our\nproposed architecture with the widely adopted FCN and also with the well known\nDeepLab-LargeFOV, DeconvNet architectures. This comparison reveals the memory\nversus accuracy trade-off involved in achieving good segmentation performance.\n  SegNet was primarily motivated by scene understanding applications. Hence, it\nis designed to be efficient both in terms of memory and computational time\nduring inference. It is also significantly smaller in the number of trainable\nparameters than other competing architectures. We also performed a controlled\nbenchmark of SegNet and other architectures on both road scenes and SUN RGB-D\nindoor scene segmentation tasks. We show that SegNet provides good performance\nwith competitive inference time and more efficient inference memory-wise as\ncompared to other architectures. We also provide a Caffe implementation of\nSegNet and a web demo at http://mi.eng.cam.ac.uk/projects/segnet/.","url_abs":"http://arxiv.org/abs/1511.00561v3","url_pdf":"http://arxiv.org/pdf/1511.00561v3.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 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