Lightweight Alternative Machine Learning Model for Automating Concrete Crack Image Classification
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OpenAlex topics: Infrastructure Maintenance and Monitoring · Concrete Corrosion and Durability · Asphalt Pavement Performance Evaluation
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References (13)
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2. Dais, D.; Bal, I. E.; Smyrou, E.; and Sarhosis, V., 2021, "Automatic Crack Classification and Segmentation on Masonry Surfaces Using Convolutional Neural Networks and Transfer Learning," Automation in Construction, V. 125, p. 103606. doi: 10.1016/j.autcon.2021.10360610.1016/j.autcon.2021.103606 ↗
3. Fu, R.; Cao, M.; Novak, D.; Qian, X.; and Alkayem, N. F., 2023, "Extended Efficient Convolutional Neural Network for Concrete Crack Detection with Illustrated Merits," Automation in Construction, V. 156, p. 105098. doi: 10.1016/j.autcon.2023.10509810.1016/j.autcon.2023.105098 ↗
4. Huyan, J.; Ma, T.; Li, W.; Yang, H.; and Xu, Z., 2022, "Pixelwise Asphalt Concrete Pavement Crack Detection via Deep Learning-Based Semantic Segmentation Method," Structural Control and Health Monitoring, V. 29, No. 8, p. e2974. doi: 10.1002/stc.297410.1002/stc.2974 ↗
5. Islam, M. M.; Hossain, M. B.; Akhtar, M. N.; Moni, M. A.; and Hasan, K. F., 2022, "CNN Based on Transfer Learning Models Using Data Augmentation and Transformation for Detection of Concrete Cracks," Algorithms, V. 15, No. 8, p. 287. doi: 10.3390/a1508028710.3390/a15080287 ↗
6. Kim, H.; Ahn, E.; Shin, M.; and Sim, S.-H., 2019, "Crack and Noncrack Classification from Concrete Surface Images Using Machine Learning," Structural Health Monitoring, V. 18, No. 3, pp. 725-738. doi: 10.1177/147592171876874710.1177/1475921718768747 ↗
7. Maalej, M.; Cherif, R.; Yaddaden, Y.; and Khoumsi, A., 2022, "Automatic Crack Detection on Concrete Structure Using a Deep Convolutional Neural Network and Transfer Learning," 2022 International Conference on Advanced Electrical Engineering (ICAEE), Constantine, Algeria. doi: 10.1109/ICAEE53772.2022.996202910.1109/ICAEE53772.2022.9962029 ↗
8. Meng, X., 2021, "Concrete Crack Detection Algorithm Based on Deep Residual Neural Networks," Scientific Programming, V. 2021, p. 3137083. doi: 10.1155/2021/313708310.1155/2021/3137083 ↗
9. Özgenel, Ç. F., 2018, "Concrete Crack Images for Classification," Mendeley Data. doi: 10.17632/5y9wdsg2zt.1
10. Rajadurai, R.-S., and Kang, S.-T., 2021, "Automated Vision-Based Crack Detection on Concrete Surfaces Using Deep Learning," Applied Sciences, V. 11, No. 11, p. 5229. doi: 10.3390/app1111522910.3390/app11115229 ↗
11. Yu, Y.; Rashidi, M.; Samali, B.; Mohammadi, M.; Nguyen, T. N.; and Zhou, X., 2022, "Crack Detection of Concrete Structures Using Deep Convolutional Neural Networks Optimized by Enhanced Chicken Swarm Algorithm," Structural Health Monitoring, V. 21, No. 5, pp. 1-15. doi: 10.1177/1475921721105354610.1177/14759217211053546 ↗
12. Zhang, L.; Yang, F.; Zhang, Y. D.; and Zhu, Y. J., 2016, "Road Crack Detection Using Deep Convolutional Neural Network," 2016 IEEE International Conference on Image Processing (ICIP), Phoenix, AZ, pp. 3708-3712. doi: 10.1109/ICIP.2016.753305210.1109/ICIP.2016.7533052 ↗
13. Zhou, Z.; Zhang, J.; and Gong, C., 2022, "Automatic Detection Method of Tunnel Lining Multi-Defects via an Enhanced You Only Look Once Network," Computer-Aided Civil and Infrastructure Engineering, V. 37, No. 6, pp. 762-780. doi: 10.1111/mice.1283610.1111/mice.12836 ↗