KARMA: Efficient Structural Defect Segmentation via Kolmogorov-Arnold Representation Learning

TL;DR

KARMA is a highly efficient semantic segmentation framework for structural defect detection that uses Kolmogorov-Arnold representations, achieving high accuracy with significantly fewer parameters and real-time inference capability.

Contribution

The paper introduces KARMA, a novel segmentation architecture based on Kolmogorov-Arnold representations, with innovations like TiKAN modules and a static-dynamic prototype mechanism for improved efficiency and performance.

Findings

Achieves competitive or superior mean IoU compared to state-of-the-art methods.

Uses 97% fewer parameters than traditional deep learning models.

Operates at 0.264 GFLOPS suitable for real-time inspection.

Abstract

Semantic segmentation of structural defects in civil infrastructure remains challenging due to variable defect appearances, harsh imaging conditions, and significant class imbalance. Current deep learning methods, despite their effectiveness, typically require millions of parameters, rendering them impractical for real-time inspection systems. We introduce KARMA (Kolmogorov-Arnold Representation Mapping Architecture), a highly efficient semantic segmentation framework that models complex defect patterns through compositions of one-dimensional functions rather than conventional convolutions. KARMA features three technical innovations: (1) a parameter-efficient Tiny Kolmogorov-Arnold Network (TiKAN) module leveraging low-rank factorization for KAN-based feature transformation; (2) an optimized feature pyramid structure with separable convolutions for multi-scale defect analysis; and (3) a static-dynamic prototype mechanism that enhances feature representation for imbalanced classes. Extensive experiments on benchmark infrastructure inspection datasets demonstrate that KARMA achieves competitive or superior mean IoU performance compared to state-of-the-art approaches, while using significantly fewer parameters (0.959M vs. 31.04M, a 97% reduction). Operating at 0.264 GFLOPS, KARMA maintains inference speeds suitable for real-time deployment, enabling practical automated infrastructure inspection systems without compromising accuracy. The source code can be accessed at the following URL: https://github.com/faeyelab/karma.