Dense Aggregation Based Efficient Network for Image Semantic Segmentation in Edge Intelligent Tasks

The image semantic segmentation algorithm required by edge intelligence needs to have high accuracy and real-time performance at the same time. In this paper, a new image segmentation network: Dense Aggregation based efficient neural network (DA-ENet) is proposed. The proposed network aggregates feature maps of each two adjacent stages of DA-ENet into an aggregation feature map, and uses shortcut connections connect each encoder stage feature map to the two decoder stages. In DA-Enet the linear propagation way of original ENet was changed. DA-Enet uses aggregations to realize feature fusion at different stages of the network and reduce the number of network parameters. DA-Enet uses shortcut connections to enhance the propagation and reuse of features. Aggregations and shortcut connections make DA-ENet a densely connected network. In densely connected network, each layer receives additional supervision from the loss function. Therefore, DA-Enet will be easier to train and has higher accuracy.The proposed DA-ENet was evaluated on three standard datasets: CamVid, CityScapes and SUN RGB-D. The experimental results demonstrate that DA-ENet has similar inference speed but higher segmentation accuracy to ENet.