A Feature Extraction Algorithm for Corner Cracks in Slabs Based on Multi-Scale Adaptive Gradient Descent
Abstract
:1. Introduction
2. Related Work
2.1. Detection Principle
2.2. Algorithm Flow
2.3. Normal Weighted Covariance Calculation
- (1)
- The weight and local position vector are calculated:
- (2)
- The weighted covariance matrix is calculated:
- (3)
- Normal calculation and normalization:
2.4. Adaptive Weighted Gradient Descent
2.4.1. Gradient Descent
2.4.2. Improved Gradient Descent Method
- (1)
- Density calculation in regard to the point cloud neighborhood:
- (2)
- Normal change rate calculation:
- (3)
- Adaptive attenuation rate calculation:
- (4)
- Comprehensive weight construction:
- (5)
- Adaptive Comprehensive Weighted Gradient Descent:
- (6)
- Multi-scale Feature Extraction and Detection:
2.5. Multi-Scale Feature Fusion
2.6. DBSCAN Clustering
3. Analysis and Discussion
3.1. Experimental Platform
3.2. Evaluation Indicators
3.3. Normal Calculation Comparison Experiment
3.4. Multi-Scale Comparative Experiment
3.5. Gradient Descent Comparison Experiment
3.6. Algorithm Comparison Experiment
4. Conclusions
- (1)
- The covariance matrix is optimized to increase the accuracy and stability of the normal calculation. The traditional PCA covariance matrix has limitations when dealing with the uneven distribution of point clouds. The improved algorithm optimizes the covariance matrix by calculating the position distance weight and position vector. It can better adapt to the non-uniformity of point cloud data, which significantly increases the accuracy and stability of the normal calculation. In the case of complex surfaces and noise interference, this optimization ensures the robustness of the normal calculation and provides a more reliable basis for subsequent crack detection;
- (2)
- Multi-scale feature extraction is used to enhance the accuracy of crack point cloud feature extraction. By changing the sigma values, the features are obtained at different scales, and the detailed features are fused. The method can capture the subtle changes in cracks at different scales and avoid the limitations of the single-scale method. Rough surfaces and cracks exhibit different characteristics at different scales. By integrating the characteristics of different scales, the algorithm can describe the geometric characteristics of cracks more comprehensively, thereby significantly increasing the accuracy of feature extraction of crack point clouds;
- (3)
- The adaptive comprehensive weighted gradient descent method is utilized to dynamically adjust the gradient descent of point clouds in different neighborhoods, while smoothing noise and strengthening local features. It not only boosts the accuracy of feature extraction, but also enhances the robustness of the algorithm as well. Through adaptive gradient descent, the algorithm can better handle noise and complex surfaces. It has a stable crack detection capability, which can effectively reduce false detections and missed detections;
- (4)
- The experimental results indicate that the precision of the improved algorithm is 0.72% higher than that of the region-growing algorithm. Compared to the RANSAC algorithm, the improved algorithm has an enhancement of 18.18% in regard to the recall rate and 11.16% in regard to the F-value, which has great crack feature extraction performance in order to extract fine cracks with rough surfaces.
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
References
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Sigma Value | Number of Feature Points Extracted |
---|---|
sigma = 0.0001 | 27,789 |
sigma = 0.0002 | 15,087 |
sigma = 0.0003 | 10,257 |
sigma = 0.0004 | 7719 |
Algorithm | Noise Ratio/% | MSE/% | MAE/% |
---|---|---|---|
① | 15 | 51.69 | 39.50 |
② | 51.37 | 38.57 | |
① | 20 | 52.53 | 41.41 |
② | 52.22 | 40.37 | |
① | 25 | 54.13 | 43.71 |
② | 53.01 | 41.93 | |
① | 30 | 55.05 | 45.47 |
② | 54.07 | 43.85 |
Algorithm | Number of Feature Points Extracted |
---|---|
Finite Difference Method | 7889 |
Nearest Neighbor Method | 12,365 |
Gaussian Weight Method | 8756 |
Improved Method | 11,927 |
Algorithm | Number of Feature Points | Number of Overlapping Point Clouds (TP) | Number of False Negative Point Clouds (FN) | Number of False Positive Point Clouds (FP) |
---|---|---|---|---|
Curvature Algorithm | 8254 | 5728 | 5673 | 2526 |
RANSAC Algorithm | 8784 | 8337 | 3064 | 447 |
Region-Growing Algorithm | 7923 | 7603 | 3798 | 320 |
Improved Algorithm | 10,767 | 10,410 | 991 | 357 |
Algorithm | Precision/% | Recall/% | F/% |
---|---|---|---|
Curvature Algorithm | 69.39 | 50.24 | 58.47 |
RANSAC Algorithm | 94.90 | 73.13 | 82.76 |
Region-Growing Algorithm | 95.96 | 66.69 | 79.57 |
Improved Algorithm | 96.68 | 91.31 | 93.92 |
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Zeng, K.; Xia, Z.; Qian, J.; Du, X.; Xiao, P.; Zhu, L. A Feature Extraction Algorithm for Corner Cracks in Slabs Based on Multi-Scale Adaptive Gradient Descent. Metals 2025, 15, 324. https://doi.org/10.3390/met15030324
Zeng K, Xia Z, Qian J, Du X, Xiao P, Zhu L. A Feature Extraction Algorithm for Corner Cracks in Slabs Based on Multi-Scale Adaptive Gradient Descent. Metals. 2025; 15(3):324. https://doi.org/10.3390/met15030324
Chicago/Turabian StyleZeng, Kai, Zibo Xia, Junlei Qian, Xueqiang Du, Pengcheng Xiao, and Liguang Zhu. 2025. "A Feature Extraction Algorithm for Corner Cracks in Slabs Based on Multi-Scale Adaptive Gradient Descent" Metals 15, no. 3: 324. https://doi.org/10.3390/met15030324
APA StyleZeng, K., Xia, Z., Qian, J., Du, X., Xiao, P., & Zhu, L. (2025). A Feature Extraction Algorithm for Corner Cracks in Slabs Based on Multi-Scale Adaptive Gradient Descent. Metals, 15(3), 324. https://doi.org/10.3390/met15030324