SegNet is a deep encoder-decoder architecture for multi-class pixel-wise segmentation that was researched and developed by members of the Computer Vision and Robotics Group (http://mi.eng.cam.ac.uk/Main/CVR) at the University of Cambridge, UK.Â
The SegNet architecture consists of an encoder network, a corresponding decoder network, and a final classification pixel-wise layer. It also consists of a series of non-linear processing layers (encoders) and a corresponding collection of decoders, accompanied by a pixel-wise classifier.
The architecture of SegNet can be seen in the following diagram:
You can also check out this diagram at https://mi.eng.cam.ac.uk/projects/segnet/.
The encoder typically consists of one or more convolutional layers with batch normalization and a ReLU, accompanied by non-overlapping max-pooling and sub-sampling. Sparse encoding, which results from the pooling process, is upsampled in the decoder...
