DUAL PYRAMIDS ENCODER-DECODER NETWORK FOR SEMANTIC SEGMENTATION IN GROUND AND AERIAL VIEW IMAGES

DUAL PYRAMIDS ENCODER-DECODER NETWORK FOR SEMANTIC SEGMENTATION IN GROUND AND AERIAL VIEW IMAGES

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Semantic segmentation is a fundamental research task in computer vision, which intends to assign a certain category to every pixel.Currently, most existing methods only utilize the deepest feature map for decoding, while high-level bushranger awning features get inevitably lost during the procedure of down-sampling.In the decoder section, transposed convolution or bilinear interpolation was widely used to restore the size of the encoded feature map; however, few optimizations are applied during up-sampling process which is detrimental to the performance for grouping and classification.In this work, rico reserve clarinet mouthpiece we proposed a dual pyramids encoder-decoder deep neural network (DPEDNet) to tackle the above issues.The first pyramid integrated and encoded multi-resolution features through sequentially stacked merging, and the second pyramid decoded the features through dense atrous convolution with chained up-sampling.

Without post-processing and multi-scale testing, the proposed network has achieved state-of-the-art performances on two challenging benchmark image datasets for both ground and aerial view scenes.