CONTI-CrackNet: A Continuity-Aware State-Space Network for Crack Segmentation
These videos have been matched automatically. Contact us if they are not relevant.
These videos have been matched automatically. Contact us if they are not relevant.
View abstract on PubMed
Summary
This summary is machine-generated.CONTI-CrackNet efficiently segments cracks in complex scenes using a novel visual state-space network. This method enhances crack continuity and detail recovery while maintaining low computational costs for practical applications.
Area Of Science
- Computer Vision
- Artificial Intelligence
- Image Processing
Background
- Crack segmentation in cluttered environments is challenging due to irregular patterns.
- Existing methods struggle to balance accuracy and computational efficiency.
Purpose Of The Study
- To develop a lightweight network for accurate and efficient crack segmentation.
- To improve the continuity and edge recovery of thin cracks in images.
Main Methods
- Introduced CONTI-CrackNet, a visual state-space network with a Multi-Directional Selective Scanning Strategy (MD3S).
- MD3S utilizes bidirectional scanning and a Bidirectional Gated Fusion (BiGF) module for enhanced global continuity.
- Proposed a Dual-Branch Pixel-Level Global-Local Fusion (DBPGL) module with Pixel-Adaptive Pooling (PAP) for detail preservation.
Main Results
- Achieved high performance on TUT (F1: 0.8332, mIoU: 0.8436) and CRACK500 (mIoU: 0.7760) datasets.
- Outperformed Convolutional Neural Network (CNN), Transformer, and Mamba baselines in crack segmentation.
- Demonstrated a favorable accuracy-efficiency balance with low GFLOPs, parameters, and high FPS (42 FPS on RTX 3090).
Conclusions
- CONTI-CrackNet effectively segments cracks, improving continuity and edge recovery for slender, irregular patterns.
- The network offers a lightweight solution with a strong balance between accuracy and computational efficiency.
- The proposed MD3S and DBPGL modules contribute to superior performance in challenging crack segmentation tasks.
These videos have been matched automatically. Contact us if they are not relevant.
These videos have been matched automatically. Contact us if they are not relevant.
Related Concept Videos
The frequency-domain technique, commonly used in analyzing and designing feedback control systems, is effective for linear, time-invariant systems. However, it falls short when dealing with nonlinear, time-varying, and multiple-input multiple-output systems. The time-domain or state-space approach addresses these limitations by utilizing state variables to construct simultaneous, first-order differential equations, known as state equations, for an nth-order system.
Consider an RLC circuit, a...
The conversion of state-space representation to a transfer function is a fundamental process in system analysis. It provides a method for transitioning from a time-domain description to a frequency-domain representation, which is crucial for simplifying the analysis and design of control systems.
The transformation process begins with the state-space representation, characterized by the state equation and the output equation. These equations are typically represented as:
Where x(t) is the...
State-space representation is a powerful tool for simulating physical systems on digital computers, necessitating the conversion of the transfer function into state-space form. Consider an nth-order linear differential equation with constant coefficients, like those encountered in an RLC circuit. The state variables are selected as the output and its n−1 derivatives. Differentiating these variables and substituting them back into the original equation produces the state equations.
In an RLC...
Basic continuous-time signals include the unit step function, unit impulse function, and unit ramp function, collectively referred to as singularity functions. Singularity functions are characterized by discontinuities or discontinuous derivatives.
The unit step function, denoted u(t), is zero for negative time values and one for positive time values, exhibiting a discontinuity at t=0. This function often represents abrupt changes, such as the step voltage introduced when turning a car's...
In signal processing, a continuous-time signal can be sampled using an impulse-train sampling technique, followed by the zero-order hold method. Impulse-train sampling involves the use of a periodic impulse train, which consists of a series of delta functions spaced at regular intervals determined by the sampling period. When a continuous-time signal is multiplied by this impulse train, it generates impulses with amplitudes corresponding to the signal's values at the sampling points.
In the...
System stability is a fundamental concept in signal processing, often assessed using convolution. For a system to be considered bounded-input bounded-output (BIBO) stable, any bounded input signal must produce a bounded output signal. A bounded input signal is one where the modulus does not exceed a certain constant at any point in time.
To determine the BIBO stability, the convolution integral is utilized when a bounded continuous-time input is applied to a Linear Time-Invariant (LTI) system....