Overcoming Overfitting Challenges with HOG Feature Extraction and XGBoost-Based Classification for Concrete Crack Monitoring
Abstract
This study proposes a method that combines Histogram of Oriented Gradients (HOG) feature extraction and Extreme Gradient Boosting (XGBoost) classification to resolve the challenges of concrete crack monitoring. The purpose of the study is to address the common issue of overfitting in machine learning models. The research uses a dataset of 40,000 images of concrete cracks and HOG feature extraction to identify relevant patterns. Classification is performed using the ensemble method XGBoost, with a focus on optimizing its hyperparameters. This study evaluates the efficacy of XGBoost in comparison to other ensemble methods, such as Random Forest and AdaBoost. XGBoost outperforms the other algorithms in terms of accuracy, precision, recall, and F1-score, as demonstrated by the results. The proposed method obtains an accuracy of 96.95% with optimized hyperparameters, a recall of 96.10%, a precision of 97.90%, and an F1-score of 97%. By optimizing the number of trees hyperparameter, 1200 trees yield the greatest performance. The results demonstrate the efficacy of HOG-based feature extraction and XGBoost for accurate and dependable classification of concrete fractures, overcoming the overfitting issues that are typically encountered in such tasks.References
K. Gao, H. Xie, Z. Li, J. Zhang, and J. Tu, “Study on eccentric behavior and serviceability performance of slender rectangular concrete columns reinforced with GFRP bars,” Compos. Struct., vol. 263, no. February, p. 113680, 2021, doi: 10.1016/j.compstruct.2021.113680.
P. Guo, W. Meng, and Y. Bao, “Automatic identification and quantification of dense microcracks in high-performance fiber-reinforced cementitious composites through deep learning-based computer vision,” Cem. Concr. Res., vol. 148, no. July, p. 106532, 2021, doi: 10.1016/j.cemconres.2021.106532.
K. Harsh, P. P. V., P. J. B., and K. Nikhil, “Implementation of Computer Vision Technique for Crack Monitoring in Concrete Structure,” J. Inst. Eng. Ser. A, vol. 104, no. 1, 2023, doi: 10.1007/s40030-022-00695-5.
X. Xie, L. Zhang, and Z. Qu, “A Critical Review of Methods for Determining the Damage States for the In-plane Fragility of Masonry Infill Walls,” J. Earthq. Eng., vol. 26, no. 9, pp. 4523–4544, 2022, doi: 10.1080/13632469.2020.1835749.
C. Yuan, B. Xiong, X. Li, X. Sang, and Q. Kong, “A novel intelligent inspection robot with deep stereo vision for three-dimensional concrete damage detection and quantification,” Struct. Heal. Monit., vol. 21, no. 3, pp. 788–802, 2022, doi: 10.1177/14759217211010238.
C. Camille, D. Kahagala Hewage, O. Mirza, and T. Clarke, “Full-scale static and single impact testing of prestressed concrete sleepers reinforced with macro synthetic fibres,” Transp. Eng., vol. 7, p. 100104, 2022, doi: 10.1016/j.treng.2022.100104.
J. Deng, Y. Lu, and V. C. S. Lee, “Concrete crack detection with handwriting script interferences using faster region-based convolutional neural network,” Comput. Civ. Infrastruct. Eng., vol. 35, no. 4, pp. 373–388, 2020, doi: 10.1111/mice.12497.
V. P. Golding, Z. Gharineiat, H. S. Munawar, and F. Ullah, “Crack Detection in Concrete Structures Using Deep Learning,” Sustain., vol. 14, no. 13, 2022, doi: 10.3390/su14138117.
H. Kim, S. Lee, E. Ahn, M. Shin, and S. H. Sim, “Crack identification method for concrete structures considering angle of view using RGB-D camera-based sensor fusion,” Struct. Heal. Monit., vol. 20, no. 2, pp. 500–512, 2021, doi: 10.1177/1475921720934758.
and R. C. L. Deng, T. Sun, L. Yang, “Binocular video-based 3D reconstruction and length quantification of cracks in concrete structures,” Autom. Constr, vol. 148, 2023, doi: 10.1016/j.autcon.2023.104743.
H. Kim, E. Ahn, M. Shin, and S. H. Sim, “Crack and Noncrack Classification from Concrete Surface Images Using Machine Learning,” Struct. Heal. Monit., vol. 18, no. 3, pp. 725–738, 2019, doi: 10.1177/1475921718768747.
N. Kheradmandi and V. Mehranfar, “A critical review and comparative study on image segmentation-based techniques for pavement crack detection,” Constr. Build. Mater., vol. 321, 2022, doi: 10.1016/j.conbuildmat.2021.126162.
S. S. N., K. S., and R. G, “Review and Analysis of Crack Detection and Classification Techniques based on Crack Types,” Int. J. Appl. Eng. Res., vol. 13, no. 8, p. 6056, 2021, doi: 10.37622/ijaer/13.8.2018.6056-6062.
R. G. Sheerin Sitara N., K. S., “Review and Analysis of Crack Detection and Classification Techniques based on Crack Types,” Int. J. Appl. Eng. Res., vol. 13, no. 8, 2021, doi: 10.37622/ijaer/13.8.2018.6056-6062.
L. Li, K. Ota, and M. Dong, “Deep Learning for Smart Industry: Efficient Manufacture Inspection System with Fog Computing,” IEEE Trans. Ind. Informatics, vol. 14, no. 10, pp. 4665–4673, 2018, doi: 10.1109/TII.2018.2842821.
B. Kim and S. Cho, “Automated vision-based detection of cracks on concrete surfaces using a deep learning technique,” Sensors (Switzerland), vol. 18, no. 10, 2018, doi: 10.3390/s18103452.
Y. Sari, P. B. Prakoso, and A. R. Baskara, “Road Crack Detection using Support Vector Machine (SVM) and OTSU Algorithm,” 2019 6th Int. Conf. Electr. Veh. Technol., pp. 349–354, 2019, doi: 10.1109/ICEVT48285.2019.8993969.
Y. Sari, P. B. Prakoso, and A. R. Baskara, “Application of neural network method for road crack detection,” Telkomnika (Telecommunication Comput. Electron. Control., vol. 18, no. 4, pp. 1962–1967, 2020, doi: 10.12928/TELKOMNIKA.V18I4.14825.
H. Hofbauer, F. Autrusseau, and A. Uhl, “Low Quality and Recognition of Image Content,” IEEE Trans. Multimed., vol. 24, pp. 3595–3610, 2022, doi: 10.1109/TMM.2021.3103394.
T. He and X. Li, “Image quality recognition technology based on deep learning,” J. Vis. Commun. Image Represent., vol. 65, p. 102654, 2019, doi: 10.1016/j.jvcir.2019.102654.
Y. Gao, L. Gao, and X. Li, “A Generative Adversarial Network Based Deep Learning Method for Low-Quality Defect Image Reconstruction and Recognition,” IEEE Trans. Ind. Informatics, vol. 17, no. 5, pp. 3231–3240, 2021, doi: 10.1109/TII.2020.3008703.
R. A. Pramunendar, D. P. Prabowo, D. Pergiwati, Y. Sari, P. N. Andono, and M. A. Soeleman, “New workflow for marine fish classification based on combination features and CLAHE enhancement technique,” Int. J. Intell. Eng. Syst., vol. 13, no. 4, pp. 293–304, 2020, doi: 10.22266/IJIES2020.0831.26.
Y. Sari, M. Alkaff, and R. A. Pramunendar, “Classification of coastal and Inland Batik using GLCM and Canberra Distance,” AIP Conf. Proc., vol. 1977, no. June 2022, 2018, doi: 10.1063/1.5042901.
Y. Sari, A. R. Baskara, and R. Wahyuni, “Classification of Chili Leaf Disease Using the Gray Level Co-occurrence Matrix (GLCM) and the Support Vector Machine (SVM) Methods,” 2021 6th Int. Conf. Informatics Comput. ICIC 2021, 2021, doi: 10.1109/ICIC54025.2021.9632920.
Y. Sari, M. Alkaff, and M. Maulida, “Classification of Rice Leaf using Fuzzy Logic and Hue Saturation Value (HSV) to Determine Fertilizer Dosage,” 2020, doi: 10.1109/ICIC50835.2020.9288585.
A. Alazba and H. Aljamaan, “Software Defect Prediction Using Stacking Generalization of Optimized Tree-Based Ensembles,” Appl. Sci., vol. 12, no. 9, 2022, doi: 10.3390/app12094577.
S. Deng, Y. Zhu, S. Duan, Z. Fu, and Z. Liu, “Stock Price Crash Warning in the Chinese Security Market Using a Machine Learning-Based Method and Financial Indicators,” Systems, vol. 10, no. 4, pp. 1–25, 2022, doi: 10.3390/systems10040108.
V. Rathakrsihman, S. B. Beddu, and A. N. Ahmed, “Comparison Studies Between Machine Learning Optimisation Technique on Predicting Concrete Compressive Strength,” Eur. PMC, pp. 1–5, 2021, doi: 10.21203/rs.3.rs-381936/v1.
J. J. Liu and J. C. Liu, “Permeability Predictions for Tight Sandstone Reservoir Using Explainable Machine Learning and Particle Swarm Optimization,” Geofluids, vol. 2022, no. 2, 2022, doi: 10.1155/2022/2263329.
D. Xiaoming, C. Ying, Z. Xiaofang, and G. Yu, “Study on Feature Engineering and Ensemble Learning for Student Academic Performance Prediction,” Int. J. Adv. Comput. Sci. Appl., vol. 13, no. 5, pp. 495–502, 2022, doi: 10.14569/IJACSA.2022.0130558.
W. Chen, H. Zhang, M. K. Mehlawat, and L. Jia, “Mean–variance portfolio optimization using machine learning-based stock price prediction,” Appl. Soft Comput., vol. 100, p. 106943, 2021, doi: 10.1016/j.asoc.2020.106943.
Y. Qiu, J. Zhou, M. Khandelwal, H. Yang, P. Yang, and C. Li, “Performance evaluation of hybrid WOA-XGBoost, GWO-XGBoost and BO-XGBoost models to predict blast-induced ground vibration,” Eng. Comput., vol. 38, no. 0123456789, pp. 4145–4162, 2022, doi: 10.1007/s00366-021-01393-9.
G. Zhou, Z. Ni, Y. Zhao, and J. Luan, “Identification of Bamboo Species Based on Extreme Gradient Boosting (XGBoost) Using Zhuhai-1 Orbita Hyperspectral Remote Sensing Imagery,” Sensors, vol. 22, no. 14, 2022, doi: 10.3390/s22145434.
A. Ibrahem Ahmed Osman, A. Najah Ahmed, M. F. Chow, Y. Feng Huang, and A. El-Shafie, “Extreme gradient boosting (Xgboost) model to predict the groundwater levels in Selangor Malaysia,” Ain Shams Eng. J., vol. 12, no. 2, pp. 1545–1556, 2021, doi: 10.1016/j.asej.2020.11.011.
C. Bentéjac, A. Csörgő, and G. Martínez-Muñoz, A comparative analysis of gradient boosting algorithms, vol. 54, no. 3. Springer Netherlands, 2021.
C. Bouchayer, J. M. Aiken, K. Thogersen, F. Renard, and T. V. Schuler, “A Machine Learning Framework to Automate the Classification of Surge-Type Glaciers in Svalbard,” JGR Earth Surf., 2022, doi: 10.1029/2022JF006597.
J. P. Tanjung and M. Muhathir, “Classification of facial expressions using SVM and HOG,” J. Informatics Telecommun. Eng., vol. 3, no. 2, pp. 210–215, 2020, doi: 10.31289/jite.v3i2.3182.
T. Tri Saputra Sibarani and C. Author, “Analysis K-Nearest Neighbors (KNN) in Identifying Tuberculosis Disease (Tb) By Utilizing Hog Feature Extraction,” Int. Comput. Sci. Inf. Technol. JournalISSN, vol. 1, no. 1, pp. 33–38, 2020.
S. T. Narasimhaiah and L. Rangarajan, “Recognition of compound characters in Kannada language,” Int. J. Electr. Comput. Eng., vol. 12, no. 6, pp. 6103–6113, 2022, doi: 10.11591/ijece.v12i6.pp6103-6113.
K. V. Greeshma and K. Sreekumar, “Fashion-MNIST classification based on HOG feature descriptor using SVM,” Int. J. Innov. Technol. Explor. Eng., vol. 8, no. 5, pp. 960–962, 2019.
S. Bakheet and A. Al-Hamadi, “A framework for instantaneous driver drowsiness detection based on improved HOG features and naïve bayesian classification,” Brain Sci., vol. 11, no. 2, pp. 1–15, 2021, doi: 10.3390/brainsci11020240.
T. Zhang et al., “HOG-ShipCLSNet: A Novel Deep Learning Network with HOG Feature Fusion for SAR Ship Classification,” IEEE Trans. Geosci. Remote Sens., vol. 60, no. June, 2022, doi: 10.1109/TGRS.2021.3082759.
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