SCNet: A simplified encoder-decoder CNN for semantic segmentation

Abstract

We present a simplified and novel fully convolutional neural network (CNN) architecture for semantic pixel-wise segmentation named as SCNet. Different from current CNN pipelines, proposed network uses only convolution layers with no pooling layer. The key objective of this model is to offer a more simplified CNN model with equal benchmark performance and results. It is an encoder-decoder based fully convolution network model. Encoder network is based on VGG 16-layer while decoder networks use upsampling and deconvolution units followed by a pixel-wise classification layer. The proposed network is simple and offers reduced search space for segmentation by using low-resolution encoder feature maps. It also offers a great deal of reduction in trainable parameters due to reusing encoder layer's sparse features maps. The proposed model offers outstanding performance and enhanced results in terms of architectural simplicity, number of trainable parameters and computational time.