关键词: 嵌套演化, 轻量化, 特征依赖, 膨胀率, DeepLabV3+

Abstract: Abstract: In order to solve the problem that DeepLabV3+ uses holes with different dilation rates, which leads to high computational complexity and low segmentation rate for some categories, this paper proposes a deep dilation bottleneck convolution ENRF (Evolutionary Nested Receptive Fields) and adaptive class-channel attention mechanism ACCA (Adaptive Class-Channel Attention) to improve the computational complexity and segmentation rate for some categories. Firstly, by combining the two mechanisms of channel adaptive attention and class adaptive attention to construct ACCA, the feature dependencies between channels and categories are fully explored, and the expression ability of key information in the feature map is effectively enhanced. Secondly, by introducing different convolution kernel sizes and dilation rates, a deep dilation bottleneck convolution ENRF based on a nested evolution network structure of receptive field is constructed, which gradually expands the receptive field of the feature map, so as to better capture multi-scale contextual information and fine-grained edge features. Finally, the ASPP module of DeepLabV3+ is replaced by the ENRF module and ACCA is added to the fused features to improve the feature expression ability. The model can achieve continuous expansion of the receptive field in the process of feature extraction, and the feature expression is more detailed. At the same time, the overall parameter amount and computational overhead of the model are effectively reduced, making DeepLabV3+ lighter. The improved DeepLabV3+ is compared with FCN8, PSPNet, UPerNet and DeepLabV3+ in terms of FLOPs, parameters (Params), and mean intersection over Union (mIoU). The experimental results show that the improved DeepLabV3+ reduces the number of parameters, reduces FLOPs, speeds up the inference speed, and improves the segmentation performance.

Key words: Nested evolution, Lightweighting, Feature dependency, Expansion rate, DeepLabV3+