Semantic Segmentation Network Based on Integral Attention

This paper aims to address issues in the field of semantic segmentation by proposing a network based on integral attention. It emphasizes the importance of accuracy and timeliness in image processing within the field of artificial intelligence, where semantic segmentation finds widespread applications in scene understanding, autonomous driving, medical image analysis, and more. Traditional methods suffer from issues such as manual feature engineering and limited contextual information, while deep learning approaches have significantly improved the performance of semantic segmentation. The proposed method enhances computational efficiency and edge region handling through CAM and SAM modules, combined with ResNet50 and PSPnet for feature extraction and decoding. Experimental results exhibit outstanding performance on the Vaihingen and Postdam datasets, particularly achieving an 85.4% accuracy on the PASCAL VOC 2012 dataset when pre-trained with MS-COCO. Ablation experiments validate the effectiveness of the proposed method, providing robust support for research and applications in the field of semantic segmentation. Overall, this paper brings important technological innovation and performance improvement to the fields of image processing and artificial intelligence.