Real-Time Segmentation Network of Yard Images Based on Improved SwiftNet
In a storage yard environment,real-time image semantic segmentation can provide intuitive scene category information.To save the limited hardware resources of edge equipment,such as industrial computers,and provide image semantic category information for multi-source information fusion,this study proposes a lightweight real-time semantic segmentation network model.First,an upsampling fusion module based on spatial attention guidance is proposed.By introducing a spatial attention and residual attention structure,a lightweight decoder is designed to restore spatial details in the upsampling restoration process,suppress redundant information,and fuse feature maps from different sources.Second,a lightweight cascaded atrous space pyramid module is proposed,which uses cascaded atrous convolution elements to enhance the network receptive field and effectively extract multi-scale features.Simultaneously,the calculation cost of multi-scale polymerization is reduced by channel splitting,channel shufflement,and channel pooling.On the publicly available Camvid dataset,the Mean Intersection over Union(MIoU)of the model is 70.1％,inference speed is 146.3 frame/s,and the segmentation accuracy and inference speed are better than those of models such as ENet and ICNet.The ablation experiment results also prove the effectiveness of the proposed modules.In the actual storage yard image dataset,the MIoU of the model is 93.5％,and the inference speed is 123.8 frame/s,proving that the model structure has good generalization performance.
万方数据
维普
