Lightweight Semantic Segmentation Network Based on Adaptive Attention Mechanism
To address the issues of slow semantic segmentation speed,low real-time performance,and high resource consumption in semantic SLAM,a lightweight Mask R-CNN network with an adaptive channel attention mechanism is proposed.Given the com-plexity of the residual networks in existing semantic segmentation networks and the relatively simple indoor application environments,this lightweight network replaces the original complex backbone ResNet-50 with a more lightweight ResNet-DS-tiny by incorporating depthwise separable convolutions and grouped convolutions.An adaptive channel attention mechanism is also introduced.In the adap-tive channel attention module,a weighted approach is used to assign different weights to the input RGB-D images from both spatial and channel dimensions,thereby enhancing the feature representation capability.Additionally,to lighten the feature pyramid,dilated convolutions are employed to expand the receptive field,effectively aggregating multi-scale features with different dilation rates.Com-pared to traditional feature pyramids,the use of dilated convolutions reduces the number of parameters.This approach not only more effectively captures RGB information features but also improves the real-time performance of the semantic segmentation system while reducing resource consumption.
万方数据
维普
