对比三种注意力U-Net模型，提升巴西岩画语义分割精度。
Evaluation of Attention Mechanisms in U-Net Architectures for Semantic Segmentation of Brazilian Rock Art Petroglyphs
- 引入残差与门控注意力模块增强特征提取能力
- 最优模型达0.710的Dice分数，召回率0.854
- 对考古遗产数字化保护有实用价值
本研究对比了三种基于U-Net的语义分割架构在巴西考古遗址岩画图像上的表现。所考察模型包括：(1) 使用边界增强高斯损失（BEGL）的BEGL-UNet；(2) 融合残差块与门控注意力机制的Attention-Residual BEGL-UNet；(3) 基于卷积块注意力模块的空间-通道注意力BEGL-UNet。所有模型均采用包含二值交叉熵与高斯边缘增强的BEGL损失函数。实验在巴西皮奥伊州波索达贝比迪尼亚考古遗址图像上进行，采用5折交叉验证。结果表明，Attention-Residual BEGL-UNet表现最佳，Dice Score为0.710，验证损失0.067，召回率0.854；空间-通道注意力模型表现接近，Dice Score为0.707，召回率0.857；基线模型BEGL-UNet Dice Score为0.690。注意力机制使分割性能相比基线提升2.5%-2.9%，证明其在考古遗产数字保存中的有效性。
原文摘要 · Abstract (English)
This study presents a comparative analysis of three U-Net-based architectures for semantic segmentation of rock art petroglyphs from Brazilian archaeological sites. The investigated architectures were: (1) BEGL-UNet with Border-Enhanced Gaussian Loss function; (2) Attention-Residual BEGL-UNet, incorporating residual blocks and gated attention mechanisms; and (3) Spatial Channel Attention BEGL-UNet, which employs spatial-channel attention modules based on Convolutional Block Attention Module. All implementations employed the BEGL loss function combining binary cross-entropy with Gaussian edge enhancement. Experiments were conducted on images from the Poço da Bebidinha Archaeological Complex, Piauí, Brazil, using 5-fold cross-validation. Among the architectures, Attention-Residual BEGL-UNet achieved the best overall performance with Dice Score of 0.710, validation loss of 0.067, and highest recall of 0.854. Spatial Channel Attention BEGL-UNet obtained comparable performance with DSC of 0.707 and recall of 0.857. The baseline BEGL-UNet registered DSC of 0.690. These results demonstrate the effectiveness of attention mechanisms for archaeological heritage digital preservation, with Dice Score improvements of 2.5-2.9% over the baseline.
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