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Image Semantic Segmentation Using Improved ENet Network

An image semantic segmentation model is proposed based on improved ENet network in order to achieve the low accuracy of image semantic segmentation in complex environment. Firstly, this paper performs pruning and convolution optimization operations on the ENet network. That is, the network structure is reasonably adjusted for better results in image segmentation by reducing the convolution operation in the decoder and proposing the bottleneck convolution structure. Squeeze-and-excitation (SE) module is then integrated into the optimized ENet network. Small-scale targets see improvement in segmentation accuracy via automatic learning of the importance of each feature channel. Finally, the experiment was verified on the public dataset. This method outperforms the existing comparison methods in mean pixel accuracy (MPA) and mean intersection over union (MIOU) values. And in a short running time, the accuracy of the segmentation and the efficiency of the operation are guaranteed. projection mapping is replaced by a Maxpooling layer with a kernel size of 2×2 and a step size of 2, and it is filled with 0 to match the size of the feature map. Conv is a 3×3 conventional convolution, expansion convolution or full convolution, and sometimes 1×5 and 5×1 asymmetric convolution are used instead.