AGMAMBA: A LIGHTWEIGHT ADAPTIVE GUIDED STATE SPACE MODEL FOR PIXEL-LEVEL CRACK SEGMENTATION IN CIVIL INFRASTRUCTURE

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Accurate pixel-level segmentation of crack regions is essential for the inspection and maintenance of civil infrastructure such as bridges, pavements, and buildings. Early detection of structural cracks enables timely maintenance actions and reduces the risk of structural failure. However, existing segmentation approaches often struggle to simultaneously capture fine crack textures and suppress complex background noise, which can significantly degrade detection accuracy. Additionally, many high-performing models require large computational resources, limiting their applicability in real-time infrastructure monitoring systems.

Challenges in Existing Crack Segmentation Methods

Current deep learning-based crack detection methods frequently encounter two primary challenges. First, cracks typically exhibit thin, irregular, and discontinuous patterns, making it difficult for models to dynamically capture their texture and morphology. Second, background elements such as shadows, stains, and surface roughness can easily be misidentified as cracks. These challenges often lead to reduced segmentation accuracy or excessive computational complexity when attempting to improve feature extraction.

AGMamba Architecture and Lightweight Design

To overcome these limitations, this study introduces AGMamba, a lightweight adaptive guided state space model designed for efficient crack segmentation. The architecture focuses on capturing crack texture features while minimizing redundant background information. With only 3.40 million parameters and 20.99 GFLOPs, AGMamba achieves a strong balance between computational efficiency and segmentation performance, making it suitable for practical civil infrastructure inspection applications.

Crack Perception Module (CPM)

A key component of the proposed model is the Crack Perception Module (CPM), which integrates two complementary mechanisms:

  • Adaptive Guided Scanning Strategy (AGSS2D) – prioritizes crack regions during feature scanning to improve the efficiency of texture extraction.

  • Selective Key Clue Modeling (SKCM) – selectively aggregates information from critical crack edges and structural features.

Together, these modules allow the network to focus on meaningful crack patterns while reducing the influence of irrelevant background features.

Frequency-Domain Feature Perception

To further enhance segmentation performance, the model incorporates a High-Low Frequency Feature Perception (HLFP) strategy and a Frequency-Domain Segmentation Head (FDSH). These components analyze differences between high-frequency crack textures and low-frequency background patterns. By leveraging frequency-domain information, the framework effectively suppresses background interference and improves crack boundary detection accuracy.

Experimental Results and Performance Evaluation

Extensive experiments were conducted on four public crack datasets, demonstrating that AGMamba consistently outperforms existing state-of-the-art (SOTA) segmentation models. On the Crack500 dataset, the proposed model achieved an F1 score of 0.7622 and an mIoU of 0.7808, representing improvements of 1.41% and 1.21%, respectively, over previous SOTA methods. These results confirm the model’s ability to achieve high segmentation accuracy while maintaining low computational cost, making it highly suitable for automated infrastructure inspection systems.

Global Civil Engineering Awards


#BridgeInspection
#PavementMonitoring
#AIinCivilEngineering
#StructuralSafety
#AutomatedInspection
#DigitalInfrastructure
#LightweightAI
#EngineeringVision
#CivilEngineeringResearch
#SmartMaintenance


 

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