a deep learning-based approach for accurate semantic segmentation with attention modules

Semantic segmentation is a fundamental task in computer vision, requiring precise object delineation for applications such as autonomous driving and medical imaging. traditional convolutional neural networks (cnns) often struggle with capturing long-range dependencies and preserving fine spatial details. it is the study’s goal to make segmentation more accurate by adding adaptive attention to the encoder and decoder stages of the u-net-based architecture. the proposed network employs resnet-50 as its backbone for efficient multi-level feature extraction. the encoder incorporates an efficient channel attention atrous spatial pyramid pooling (eca-aspp) module to enhance its context representation. this module uses dilated convolutions and adaptive channel attention to improve the collection of features at different sizes. there is also a point-wise spatial attention (psa) module in the decoder that dynamically gathers global contextual information while keeping fine-grained spatial details. extensive experiments on the stanford background dataset demonstrate a consistent improvement across all segmentation categories. the best-performing model achieves a mean intersection over union (miou) of 78.65%, outperforming baseline approaches. furthermore, evaluation on the cityscapes dataset yields an miou of 80.46%, surpassing state-of-the-art methods such as deeplabv3 and danet. these results show that using adaptive attention during both the encoding and decoding steps works well, finding a good balance between accurate segmentation and fast computing. the proposed network demonstrates strong potential for real-world applications requiring precise segmentation.