An improved Deeplabv3+ semantic segmentation algorithm with multiple loss constraints

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Abstract
Aiming at the problems of low segmentation accuracy and inaccurate object boundary segmentation in current semantic segmentation algorithms, a semantic segmentation algorithm using multiple loss function constraints and multi-level cascading residual structure is proposed. The multi-layer cascaded residual unit was used to increase the range of the network layer receptive field. A parallel network was constructed to extract different depth feature information, then different depth feature information and encoder output features are fused to obtain multiple outputs feature which build multiple losses with the label, thereby constraining the model to optimize the network. The proposed network was evaluated on Cityscapes and CamVid datasets. The experimental results show that the mean Intersection over Union ratio (MIoU) of the proposed algorithm is 3.07% and 3.59% higher than the original Deeplabv3+ algorithm, respectively.

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