A Deep Learning-based Approach for Accurate Semantic Segmentation with Attention Modules

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Sahragard E.; Farsi H.; Mohamadzadeh S.

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Journal Paper

Paper Information

Journal: Iranica Journal of Energy and Environment Year:2025 | Volume:16 | Issue:4 Page(s): 692-705

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Title

A Deep Learning-based Approach for Accurate Semantic Segmentation with Attention Modules

Author(s)

Sahragard E. | Farsi H. | Mohamadzadeh S. | Issue Writer Certificate

Keywords

Atrous Spatial Pyramid Pooling

Efficient channel attention

Point-wise Spatial Attention

Semantic segmentation

Abstract

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.

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