Efficient Residual Neural Network for Semantic Segmentation

In this paper, we present an improved Efficient Neural Network (ENet) for semantic segmentation, and named the proposed network as Efficient Residual Neural Network (ERNet). The ERNet network contains two processing streams: one is pooling stream, which is used to obtain high-dimensional semantic information; the other is residual stream which is used to record low-dimensional boundary information. The ERNet has five stages, each stage contains several bottleneck modules. The output of each bottleneck in the ERNet network is fed into the residual stream. Starting from the second stage of ERNet, pooling stream and residual stream through concatenating are used as inputs for each down-sampling or up-sampling bottleneck. The identity mapping of residual stream shortens the distance between the near input and output terminals of each stage network in ERNet, alleviates the problem of vanishing gradient, strengthens the propagation of low-dimensional boundary features, and encourages feature reuse of low-dimensional boundary features. We tested ERNet on CamVid, Cityscape, and SUN RGB-D datasets. The segmentation speed of ERNet is close to that of ENet, but the segmentation accuracy is higher than that of ENet.