Kamusal Alanlarda Transfer Öğrenme Tabanlı Hasar Tespiti

Yıl 2025, Cilt: 4 Sayı: 2, 290 - 306, 26.06.2025

Tuğçe Keleş , Süha Temur , Furkan Kılınç , Mehmet Veysel Gün , Sengul Dogan , Türker Tuncer

https://doi.org/10.62520/fujece.1583372

https://izlik.org/JA82YY92AL

Öz

Kentlerde hızla artan nüfus ve yoğun kentleşme süreci, kamusal alanların etkin yönetimini ve bu alanlardaki altyapının sürdürülebilirliğini önemli hale getirmiştir. Bu süreç, kent yönetimlerini kamusal alanlardaki hasarların hızlı ve doğru bir şekilde tespiti için yenilikçi çözümler aramaya itmiştir. Geleneksel hasar tespit yöntemleri yavaş ve maliyetli olup, büyük kentlerin dinamik yapısı karşısında yetersiz kalmaktadır. Bu durum kentsel güvenliği ve yaşam kalitesini olumsuz etkilemektedir. Bu noktada, derin öğrenme ve yapay zeka teknolojilerinin hasar tespit süreçlerini otomatik hale getirerek bu soruna bir çözüm sunduğu görülmektedir. Bu çalışmada, kentlerdeki kamusal alanlardaki hasarların otomatik olarak tespiti için yapay zeka tabanlı bir sistem geliştirilmiştir. Düşük kaynak gereksinimi ve elde ettiği yüksek başarı oranı ile MobileNetv2 modeli kullanılmıştır. Veri kümesinin sınırlı olması nedeniyle meydana gelebilecek aşırı uyum sorununu önlemek için veri artırma yöntemleri uygulanmıştır. Model, doğruluk, hassasiyet, geri çağırma ve F1 skoru açısından sırasıyla %83,33, %84,20, %83,30 ve %83,70 başarı elde etmiştir. Bu sonuçlar sayesinde modelin farklı hasar tiplerini iyi bir oranda tespit ettiği görülmektedir. Bu çalışmanın sonuçları, günümüzün hızla kentleşen dünyasında yenilikçi bir çözüm sunmaktadır. Bu çözüm altyapı unsurlarında meydana gelen hasarları hızlı ve etkili bir şekilde tespit ederek şehir yönetimlerine etkili bir yol haritası sunacaktır. Bu durum, hızlı kentleşmenin getirdiği sorunların çözülmesine olanak tanır. Bu kapsamda gerçekleştirilen çalışma hem teorik hem de pratik açıdan önemli bir değer taşımaktadır.

Anahtar Kelimeler

Yapay zeka , Kamusal alanlar , Hasar tespiti

Etik Beyan

Hazırlanan makale için etik kurul izni alınmasına gerek yoktur. Hazırlanan makalede herhangi bir kişi/kurumla çıkar çatışması bulunmamaktadır

Transfer Learning Based Damage Detection in Public Areas

https://izlik.org/JA82YY92AL

Öz

The rapidly increasing population and dense urbanization process in cities have made the effective management of public spaces and the sustainability of the infrastructure in these areas important. This process has led city administrations to seek innovative solutions for rapid and accurate detection of damage in public spaces. Traditional damage detection methods are slow and costly, and are insufficient in the face of the dynamic structure of large cities. This situation negatively affects urban security and quality of life. At this point, it is seen that deep learning and artificial intelligence technologies offer a solution to this problem by automating damage detection processes. In this study, an artificial intelligence-based system has been developed for automatic detection of damage in urban public spaces. The MobileNetv2 model was used with its low resource requirement and high success rate. Data augmentation methods were applied to prevent the overfitting problem that may occur due to the limited dataset. The model achieved 83.33%, 84.20%, 83.30% and 83.70% success in terms of accuracy, precision, recall and F1 score, respectively. These findings demonstrate that, the model detects different damage types at a good rate. The results of this study provide an innovative solution in today's rapidly urbanizing world. This solution will provide an effective roadmap to city administrations by quickly and effectively detecting damage to infrastructure elements. This facilitates addressing challenges caused by rapid urbanization. The study carried out in this context has significant value both theoretically and practically.

Anahtar Kelimeler

Artificial intelligence , Public areas , Damage detection

Etik Beyan

There is no need to obtain ethics committee permission for the prepared article. There is no conflict of interest with any person/institution in the prepared article.

Kaynakça

• C. A. Vogt, K. L. Andereck, and K. Pham, "Designing for quality of life and sustainability," Ann. Tour. Res., vol. 83, p. 102963, 2020.
• S. M. Low, Why Public Space Matters. Oxford, U.K.: Oxford Univ. Press, 2023.
• M. Reba and K. C. Seto, "A systematic review and assessment of algorithms to detect, characterize, and monitor urban land change," Remote Sens. Environ., vol. 242, p. 111739, 2020.
• H. S. Munawar et al., "Image-based crack detection methods: A review," Infrastructures, vol. 6, no. 8, p. 115, 2021.
• Z. Li et al., "A survey of convolutional neural networks: analysis, applications, and prospects," IEEE Trans. Neural Netw. Learn. Syst., vol. 33, no. 12, pp. 6999–7019, 2021.
• V. Pham, C. Pham, and T. Dang, "Road damage detection and classification with detectron2 and faster R-CNN," in Proc. IEEE Int. Conf. Big Data (Big Data), 2020, pp. 5592–5601.
• S. Shim et al., "Road damage detection using super-resolution and semi-supervised learning with generative adversarial network," Autom. Constr., vol. 135, p. 104139, 2022.
• R. Bibi et al., "Edge AI-based automated detection and classification of road anomalies in VANET using deep learning," Comput. Intell. Neurosci., vol. 2021, no. 1, p. 6262194, 2021.
• A. Kyslytsyna et al., "Road surface crack detection method based on conditional generative adversarial networks," Sensors, vol. 21, no. 21, p. 7405, 2021.
• W. Ye et al., "Deep learning-based fast detection of apparent concrete crack in slab tracks with dilated convolution," Constr. Build. Mater., vol. 329, p. 127157, 2022.
• Q. Zou et al., "CrackTree: Automatic crack detection from pavement images," Pattern Recognit. Lett., vol. 33, no. 3, pp. 227–238, 2012.
• Z. Fan et al., "Automatic crack detection on road pavements using encoder-decoder architecture," Materials, vol. 13, no. 13, p. 2960, 2020.
• V. Mandal, L. Uong, and Y. Adu-Gyamfi, "Automated road crack detection using deep convolutional neural networks," in Proc. IEEE Int. Conf. Big Data, 2018, pp. 5212–5215.
• L. Zhang, F. Yang, Y. D. Zhang, and Y. J. Zhu, "Road crack detection using deep convolutional neural network," in Proc. IEEE Int. Conf. Image Process. (ICIP), 2016, pp. 3708–3712.
• L. Perez and J. Wang, "The effectiveness of data augmentation in image classification using deep learning," arXiv preprint, arXiv:1712.04621, 2017.
• K. He, X. Zhang, S. Ren, and J. Sun, "Deep residual learning for image recognition," in Proc. IEEE Conf. Comput. Vis. Pattern Recognit., 2016, pp. 770–778.
• M. Sandler et al., "MobileNetV2: Inverted residuals and linear bottlenecks," in Proc. IEEE Conf. Comput. Vis. Pattern Recognit., 2018, pp. 4510–4520.
• C. Szegedy et al., "Going deeper with convolutions," in Proc. IEEE Conf. Comput. Vis. Pattern Recognit., 2015, pp. 1–9.
• A. Krizhevsky, I. Sutskever, and G. E. Hinton, "ImageNet classification with deep convolutional neural networks," in Adv. Neural Inf. Process. Syst., vol. 25, 2012.
• C. Szegedy, S. Ioffe, V. Vanhoucke, and A. Alemi, "Inception-v4, Inception-ResNet and the impact of residual connections on learning," in Proc. AAAI Conf. Artif. Intell., vol. 31, no. 1, 2017.
• G. Huang, Z. Liu, L. Van Der Maaten, and K. Q. Weinberger, "Densely connected convolutional networks," in Proc. IEEE Conf. Comput. Vis. Pattern Recognit., 2017, pp. 4700–4708.
• C. Szegedy, V. Vanhoucke, S. Ioffe, J. Shlens, and Z. Wojna, "Rethinking the Inception architecture for computer vision," in Proc. IEEE Conf. Comput. Vis. Pattern Recognit., 2016, pp. 2818–2826.
• A. Farhadi and J. Redmon, "YOLOv3: An incremental improvement," in Comput. Vis. Pattern Recognit., vol. 1804, Springer, pp. 1–6, 2018.
• A. Khan, A. Sohail, U. Zahoora, and A. S. Qureshi, "A survey of the recent architectures of deep convolutional neural networks," Artif. Intell. Rev., vol. 53, pp. 5455–5516, 2020.
• J. Goldberger, G. E. Hinton, S. Roweis, and R. R. Salakhutdinov, "Neighbourhood components analysis," in Adv. Neural Inf. Process. Syst., vol. 17, 2004.
• Y. Zhai et al., "A chi-square statistics based feature selection method in text classification," in Proc. IEEE Int. Conf. Softw. Eng. Service Sci. (ICSESS), 2018, pp. 160–163.
• R. Aggarwal and S. Kumar, "Classification model for meticulous presaging of heart disease through NCA using machine learning," Evol. Intell., vol. 16, no. 5, pp. 1689–1698, 2023.
• T. Evgeniou and M. Pontil, "Support vector machines: Theory and applications," in Adv. Course Artif. Intell., Springer, pp. 249–257, 1999.
• M. Hossin and M. N. Sulaiman, "A review on evaluation metrics for data classification evaluations," Int. J. Data Mining Knowl. Manag. Process, vol. 5, no. 2, p. 1, 2015.
• Z. Xu et al., "Pavement crack detection from CCD images with a locally enhanced transformer network," Int. J. Appl. Earth Obs. Geoinf., vol. 110, p. 102825, 2022.
• G. X. Hu et al., "Pavement crack detection method based on deep learning models," Wireless Commun. Mobile Comput., vol. 2021, no. 1, p. 5573590, 2021.
• J. Huyan et al., "CrackU-net: A novel deep convolutional neural network for pixelwise pavement crack detection," Struct. Control Health Monit., vol. 27, no. 8, p. e2551, 2020.
• T. Chen et al., "Pavement crack detection and recognition using the architecture of SegNet," J. Ind. Inf. Integr., vol. 18, p. 100144, 2020.
• J. Liu et al., "Automated pavement crack detection and segmentation based on two-step convolutional neural network," Comput.-Aided Civ. Infrastruct. Eng., vol. 35, no. 11, pp. 1291–1305, 2020.
• Y. Djenouri et al., "Intelligent graph convolutional neural network for road crack detection," IEEE Trans. Intell. Transp. Syst., vol. 24, no. 8, pp. 8475–8482, 2022.
• A. Ahmadi, S. Khalesi, and A. Golroo, "An integrated machine learning model for automatic road crack detection and classification in urban areas," Int. J. Pavement Eng., vol. 23, no. 10, pp. 3536–3552, 2022.
• K. Hacıefendioğlu and H. B. Başağa, "Concrete road crack detection using deep learning-based faster R-CNN method," Iran. J. Sci. Technol. Trans. Civ. Eng., vol. 46, no. 2, pp. 1621–1633, 2022.
• N. H. T. Nguyen et al., "Two-stage convolutional neural network for road crack detection and segmentation," Expert Syst. Appl., vol. 186, p. 115718, 2021.