FORTRESS通过融合深度可分离卷积与柯尔莫哥洛夫网络，实现高精度实时结构缺陷分割。
FORTRESS: Function-composition Optimized Real-Time Resilient Structural Segmentation via Kolmogorov-Arnold Enhanced Spatial Attention Networks
- 采用深度可分离卷积与自适应柯尔莫哥洛夫网络组合，优化模型结构。
- 参数量减少91%（3100万→290万），计算量降为1.17 GFLOPs，推理速度提升3倍。
- 适用于资源受限环境，适合工程巡检等实时缺陷识别场景。
在民用基础设施自动化缺陷分割中，如何兼顾高精度与计算效率以实现实时部署是一个关键挑战。本文提出FORTRESS（Function-composition Optimized Real-Time Resilient Structural Segmentation），通过将深度可分离卷积与自适应柯尔莫哥洛夫-阿诺德网络（TiKAN）结合，实现精度与速度的平衡。其三大创新包括：每层参数量减少3.6倍的深度可分离卷积框架；仅在计算收益显著时才启用函数组合变换的自适应TiKAN集成；以及跨解码器层级融合空间、通道与KAN增强特征的多尺度注意力机制。该架构实现91%参数量压缩（从31M降至2.9M），计算复杂度降低至1.17 GFLOPs（原13.7 GFLOPs），推理速度提升3倍，同时保持优异分割性能。在基准数据集上的评估显示，其F1-score达0.771，平均IoU为0.677，显著优于U-Net、SA-UNet和U-KAN等现有方法。双优化策略对性能至关重要，验证了FORTRESS在资源受限场景下兼具准确率与高效性的实用性。完整架构细节见补充材料，代码已公开于https://github.com/faeyelab/fortress-paper-code。
原文摘要 · Abstract (English)
Automated structural defect segmentation in civil infrastructure faces a critical challenge: achieving high accuracy while maintaining computational efficiency for real-time deployment. This paper presents FORTRESS (Function-composition Optimized Real-Time Resilient Structural Segmentation), a new architecture that balances accuracy and speed by using a special method that combines depthwise separable convolutions with adaptive Kolmogorov-Arnold Network integration. FORTRESS incorporates three key innovations: a systematic depthwise separable convolution framework achieving a 3.6x parameter reduction per layer, adaptive TiKAN integration that selectively applies function composition transformations only when computationally beneficial, and multi-scale attention fusion combining spatial, channel, and KAN-enhanced features across decoder levels. The architecture achieves remarkable efficiency gains with 91% parameter reduction (31M to 2.9M), 91% computational complexity reduction (13.7 to 1.17 GFLOPs), and 3x inference speed improvement while delivering superior segmentation performance. Evaluation on benchmark infrastructure datasets demonstrates state-of-the-art results with an F1- score of 0.771 and a mean IoU of 0.677, significantly outperforming existing methods including U-Net, SA-UNet, and U- KAN. The dual optimization strategy proves essential for optimal performance, establishing FORTRESS as a robust solution for practical structural defect segmentation in resource-constrained environments where both accuracy and computational efficiency are paramount. Comprehensive architectural specifications are provided in the Supplemental Material. Source code is available at URL: https://github.com/faeyelab/fortress-paper-code.
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