SegNet

SegNet: A deep convolutional encoder-decoder architecture for image segmentation. We present a novel and practical deep fully convolutional neural network architecture for semantic pixel-wise segmentation termed SegNet. This core trainable segmentation engine consists of an encoder network, a corresponding decoder network followed by a pixel-wise classification layer. The architecture of the encoder network is topologically identical to the 13 convolutional layers in the VGG16 network. The role of the decoder network is to map the low resolution encoder feature maps to full input resolution feature maps for pixel-wise classification. The novelty of SegNet lies is in the manner in which the decoder upsamples its lower resolution input feature map(s). Specifically, the decoder uses pooling indices computed in the max-pooling step of the corresponding encoder to perform non-linear upsampling. This eliminates the need for learning to upsample. The upsampled maps are sparse and are then convolved with trainable filters to produce dense feature maps. We compare our proposed architecture with the widely adopted FCN and also with the well known DeepLab-LargeFOV, DeconvNet architectures. This comparison reveals the memory versus accuracy trade-off involved in achieving good segmentation performance. SegNet was primarily motivated by scene understanding applications. Hence, it is designed to be efficient both in terms of memory and computational time during inference. It is also significantly smaller in the number of trainable parameters than other competing architectures. We also performed a controlled benchmark of SegNet and other architectures on both road scenes and SUN RGB-D indoor scene segmentation tasks. We show that SegNet provides good performance with competitive inference time and more efficient inference memory-wise as compared to other architectures. We also provide a Caffe implementation of SegNet and a web demo at this http URL http://mi.eng.cam.ac.uk/projects/segnet/


References in zbMATH (referenced in 27 articles )

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  1. Alberto Gomez: MIPROT: A Medical Image Processing Toolbox for MATLAB (2021) arXiv
  2. Chung, Eric; Leung, Wing Tat; Pun, Sai-Mang; Zhang, Zecheng: A multi-stage deep learning based algorithm for multiscale model reduction (2021)
  3. Jia, Fan; Liu, Jun; Tai, Xue-Cheng: A regularized convolutional neural network for semantic image segmentation (2021)
  4. Khatri, Rajendra K. C.; Caseria, Brendan J.; Lou, Yifei; Xiao, Guanghua; Cao, Yan: Automatic extraction of cell nuclei using dilated convolutional network (2021)
  5. Patel, Dhruv V.; Oberai, Assad A.: GAN-based priors for quantifying uncertainty in supervised learning (2021)
  6. Philippe Apparicio, David Maignan, Jérémy Gelb: VIFECO: An Open-Source Software for Counting Features on a Video (2021) not zbMATH
  7. Shen, Weihao; Xu, Wenbo; Zhang, Hongyang; Sun, Zexin; Ma, Jianxiong; Ma, Xinlong; Zhou, Shoujun; Guo, Shijie; Wang, Yuanquan: Automatic segmentation of the femur and tibia bones from X-ray images based on pure dilated residual U-Net (2021)
  8. Van Biesbroeck, Antoine; Shang, Feifei; Bassir, David: CAD model segmentation via deep learning (2021)
  9. Wang, Detao; Chen, Guoxiong: Seismic stratum segmentation using an encoder-decoder convolutional neural network (2021)
  10. Yuan, Yuhui; Huang, Lang; Guo, Jianyuan; Zhang, Chao; Chen, Xilin; Wang, Jingdong: OCNet: object context for semantic segmentation (2021)
  11. Zhang, Xiaofeng; Sun, Yujuan; Liu, Hui; Hou, Zhongjun; Zhao, Feng; Zhang, Caiming: Improved clustering algorithms for image segmentation based on non-local information and back projection (2021)
  12. Amosov, O. S.; Amosova, S. G.; Zhiganov, S. V.; Ivanov, Yu. S.; Pashchenko, F. F.: Computational method for recognizing situations and objects in the frames of a continuous video stream using deep neural networks for access control systems (2020)
  13. Hu, Ruimeng: Deep learning for ranking response surfaces with applications to optimal stopping problems (2020)
  14. Jia, Fan; Tai, Xue-Cheng; Liu, Jun: Nonlocal regularized CNN for image segmentation (2020)
  15. Pahič, Rok; Ridge, Barry; Gams, Andrej; Morimoto, Jun; Ude, Aleš: Training of deep neural networks for the generation of dynamic movement primitives (2020)
  16. Valada, Abhinav; Mohan, Rohit; Burgard, Wolfram: Self-supervised model adaptation for multimodal semantic segmentation (2020)
  17. Wang, Yong; Zhang, Dongfang; Dai, Guangming: Classification of high resolution satellite images using improved U-Net (2020)
  18. Zhang, Hai-Miao; Dong, Bin: A review on deep learning in medical image reconstruction (2020)
  19. Fan, Yuwei; Feliu-Fabà, Jordi; Lin, Lin; Ying, Lexing; Zepeda-Núñez, Leonardo: A multiscale neural network based on hierarchical nested bases (2019)
  20. Fan, Yuwei; Lin, Lin; Ying, Lexing; Zepeda-Núñez, Leonardo: A multiscale neural network based on hierarchical matrices (2019)

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