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With the limitations of manual inspection methods, such as being labor intensive, time consuming, and prone to human error, automated detection techniques have emerged as efficient and scalable alternatives. This study presents a detailed comparative analysis of state-of-the-art crack detection models, i.e., YOLOv7, VGG-19, ResNet-50, Naive Bayes, and deep convolutional neural networks, evaluating their performance on diverse and complex pavement image datasets. To ensure fairness and consistency, all models were trained and tested under identical conditions. ResNet-50 demonstrated superior performance, achieving the highest accuracy, that is, 99.8% in detecting and segmenting cracks in a variety of pavement scenarios. Its ability to balance precision and robustness makes it a leading solution for automated crack detection.<\/jats:p>","DOI":"10.1177\/14485869251362458","type":"journal-article","created":{"date-parts":[[2025,8,4]],"date-time":"2025-08-04T05:53:53Z","timestamp":1754286833000},"page":"193-204","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":0,"title":["Comparative Analysis of Deep Learning Models for Efficient Crack Detection in Concrete Surfaces"],"prefix":"10.1177","volume":"21","author":[{"ORCID":"https:\/\/orcid.org\/0009-0000-6922-3614","authenticated-orcid":false,"given":"Satakshi","family":"Verma","sequence":"first","affiliation":[{"name":"Department of Computer Science and Engineering, Thapar Institute of Engineering and Technology, Patiala, India"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0005-6617-0373","authenticated-orcid":false,"given":"Bajrangi","family":"Kumar Mishra","sequence":"additional","affiliation":[{"name":"Department of Civil Engineering, Thapar Institute of Engineering and Technology, Patiala, India"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8563-6611","authenticated-orcid":false,"given":"Anu","family":"Bajaj","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering, Thapar Institute of Engineering and Technology, Patiala, India"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7740-9029","authenticated-orcid":false,"given":"Abhinay","family":"Kumar","sequence":"additional","affiliation":[{"name":"Department of Civil Engineering, Thapar Institute of Engineering and Technology, Patiala, India"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"179","published-online":{"date-parts":[[2025,8,3]]},"reference":[{"key":"e_1_3_2_2_1","unstructured":"\u00c7a\u011flar F\u0131rat \u00d6zgenel (2019). Concrete Crack Images for Classification. Accessed: 2025-01-23."},{"key":"e_1_3_2_3_1","doi-asserted-by":"crossref","unstructured":"Alshami A. Alflih R. Almushaigh R. Alhasson H. (2022). An automatic road crack detection system. In 2022 2nd International conference on computing and information technology (ICCIT) (pp. 175\u2013179).","DOI":"10.1109\/ICCIT52419.2022.9711615"},{"key":"e_1_3_2_4_1","doi-asserted-by":"crossref","unstructured":"Anzum H. Sammo M. N. S. Akhter S. (2024). Leveraging data efficient image transformer (DeIT) for road crack detection and classification. In 2024 International conference on advances in computing communication electrical and smart systems (iCACCESS) (pp. 1\u20136).","DOI":"10.1109\/iCACCESS61735.2024.10499539"},{"key":"e_1_3_2_5_1","doi-asserted-by":"crossref","unstructured":"Carr T. A. Jenkins M. D. Iglesias M. I. Buggy T. Morison G. (2018). Road crack detection using a single stage detector based deep neural network. 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In 2024 IEEE 2nd International conference on sensors electronics and computer engineering (ICSECE) (pp. 43\u201347).","DOI":"10.1109\/ICSECE61636.2024.10729401"},{"key":"e_1_3_2_16_1","doi-asserted-by":"crossref","unstructured":"Ma M. Song S. Li H. Zhang B. (2024). Research on road crack detection in complex background based on MFF-YOLO. In 2024 4th International conference on neural networks information and communication engineering (NNICE) (pp. 919\u2013924).","DOI":"10.1109\/NNICE61279.2024.10498814"},{"key":"e_1_3_2_17_1","doi-asserted-by":"crossref","unstructured":"Mandal V. Uong L. Adu-Gyamfi Y. (2018). Automated road crack detection using deep convolutional neural networks. In 2018 IEEE international conference on big data (Big Data) (pp. 5212\u20135215).","DOI":"10.1109\/BigData.2018.8622327"},{"key":"e_1_3_2_18_1","doi-asserted-by":"crossref","unstructured":"Medvedev M. Pavlov V. (2020). Road surface marking recognition and road surface quality evaluation using convolution neural network. In 2020 International multi-conference on industrial engineering and modern technologies (FarEastCon) (pp. 1\u20133).","DOI":"10.1109\/FarEastCon50210.2020.9271368"},{"key":"e_1_3_2_19_1","doi-asserted-by":"crossref","unstructured":"Miao X. Liu B. (2023). YOLOv5s detection method for road cracks based on residual and attention mechanism. In 2023 Asia conference on advanced robotics automation and control engineering (ARACE) (pp. 180\u2013184).","DOI":"10.1109\/ARACE60380.2023.00035"},{"key":"e_1_3_2_20_1","doi-asserted-by":"crossref","unstructured":"Nayyeri F. Zhou J. (2021). Multi-resolution ResNet for road and bridge crack detection. In 2021 Digital image computing: Techniques and applications (DICTA) (pp. 1\u20138).","DOI":"10.1109\/DICTA52665.2021.9647398"},{"key":"e_1_3_2_21_1","doi-asserted-by":"crossref","unstructured":"Nie M. Wang C. (2019). Pavement crack detection based on YOLO v3. In 2019 2nd International conference on safety produce informatization (IICSPI) (pp. 327\u2013330).","DOI":"10.1109\/IICSPI48186.2019.9095956"},{"key":"e_1_3_2_22_1","doi-asserted-by":"crossref","unstructured":"\u00d6d\u00fcbek E. Atik M. E. (2024). Detection of asphalt pavement cracks with YOLO architectures from unmanned aerial vehicle images. In 2024 32nd Signal processing and communications applications conference (SIU) (pp. 1\u20134).","DOI":"10.1109\/SIU61531.2024.10601031"},{"key":"e_1_3_2_23_1","doi-asserted-by":"crossref","unstructured":"Pei J. Wu X. Liu X. (2024). YOLO-RDD: A road defect detection algorithm based on YOLO. In 2024 27th International conference on computer supported cooperative work in design (CSCWD) (pp. 1695\u20131703).","DOI":"10.1109\/CSCWD61410.2024.10580137"},{"key":"e_1_3_2_24_1","doi-asserted-by":"crossref","unstructured":"Pereira V. Tamura S. Hayamizu S. Fukai H. (2018). 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