{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,26]],"date-time":"2026-06-26T05:47:20Z","timestamp":1782452840861,"version":"3.54.5"},"reference-count":52,"publisher":"MDPI AG","issue":"12","license":[{"start":{"date-parts":[[2026,6,18]],"date-time":"2026-06-18T00:00:00Z","timestamp":1781740800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Sichuan Provincial Engineering Research Center of Smart Operation and Maintenance of Civil Aviation Airports","award":["JCZX2024ZZ16"],"award-info":[{"award-number":["JCZX2024ZZ16"]}]},{"DOI":"10.13039\/501100012226","name":"Fundamental Research Funds for the Central Universities","doi-asserted-by":"publisher","award":["25CAFUC03036, 538 25CAFUC03037, 25CAFUC03038, and 25CAFUC09010"],"award-info":[{"award-number":["25CAFUC03036, 538 25CAFUC03037, 25CAFUC03038, and 25CAFUC09010"]}],"id":[{"id":"10.13039\/501100012226","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Crack detection in mobile inspection scenarios is constrained by both the extremely slender geometry of crack targets and the real-time inference requirements on edge devices, which expose systematic limitations of general-purpose object detectors. This paper proposes YOLO-Crack, a closed-loop solution that couples geometry-statistics-driven module design with end-to-end edge deployment validation. On the algorithmic side, we first quantify crack geometric properties and then introduce (i) a crack-aware cross-dimensional fusion attention (CFCA) module to strengthen feature representations, (ii) a dual-path feature enhancement module (DFEM) to preserve fine details during upsampling, and (iii) an empirical smooth quality window adjustment with shape consistency regularization to stabilize bounding-box regression for slender cracks. Experiments on the Crack500 dataset show that YOLO-Crack achieves 78.8% precision, 51.4% recall, and 65.7% mAP@0.5, improving over the YOLOv11n baseline by 4.2, 1.7, and 2.9 percentage points, respectively. On the engineering side, we deploy YOLO-Crack on a Jetson Orin NX mobile robot platform and evaluate it in a real ROS pipeline; the measured end-to-end throughput reaches 25.5 FPS, meeting real-time video processing requirements. The proposed framework provides a practical reference workflow for edge vision tasks, from geometry analysis to engineering verification.<\/jats:p>","DOI":"10.3390\/s26123892","type":"journal-article","created":{"date-parts":[[2026,6,19]],"date-time":"2026-06-19T13:04:04Z","timestamp":1781874244000},"page":"3892","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["YOLO-Crack: Geometry-Guided Real-Time Crack Detection Framework Toward Edge Deployment"],"prefix":"10.3390","volume":"26","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6625-2209","authenticated-orcid":false,"given":"Zhe","family":"Wei","sequence":"first","affiliation":[{"name":"School of Computer Science and Artificial Intelligence, Civil Aviation Flight University of China, Guanghan 618307, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Rui","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Computer Science and Artificial Intelligence, Civil Aviation Flight University of China, Guanghan 618307, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Rong","family":"Dai","sequence":"additional","affiliation":[{"name":"School of Computer Science and Artificial Intelligence, Civil Aviation Flight University of China, Guanghan 618307, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Haibo","family":"Xu","sequence":"additional","affiliation":[{"name":"Guanghan Flight College, Civil Aviation Flight University of China, Guanghan 618307, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Huan","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Computer Science and Artificial Intelligence, Civil Aviation Flight University of China, Guanghan 618307, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0002-1095-0504","authenticated-orcid":false,"given":"Yurong","family":"Zou","sequence":"additional","affiliation":[{"name":"School of Computer Science and Artificial Intelligence, Civil Aviation Flight University of China, Guanghan 618307, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,6,18]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Dong, X., Liu, Y., and Dai, J. (2024). Concrete surface crack detection algorithm based on improved YOLOv8. Sensors, 24.","DOI":"10.3390\/s24165252"},{"key":"ref_2","first-page":"583","article-title":"A deep residual network designed for detecting cracks in buildings of historical significance","volume":"15","author":"Makhanova","year":"2024","journal-title":"Int. J. Adv. Comput. Sci. Appl."},{"key":"ref_3","unstructured":"Amenc, N., Blanc-Brude, F., Jayles, B., Gupta, A., Marcelo, D., and Orminski, J. (2024). Highway to hell: Climate risks will cost hundreds of billions to investors in infrastructure before 2050. SSRN Electron. J."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"10013","DOI":"10.1007\/s11042-024-18998-z","article-title":"IDSDeep-CCD: Intelligent decision support system based on deep learning for concrete cracks detection","volume":"84","author":"Abualigah","year":"2025","journal-title":"Multimed. Tools Appl."},{"key":"ref_5","first-page":"liad023","article-title":"Review and assessment of technical and legal challenges in application of unmanned aerial vehicles in monitoring and inspection of bridges","volume":"2","author":"Adibfar","year":"2023","journal-title":"Intell. Transp. Infrastruct."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"e13784","DOI":"10.1111\/exsy.13784","article-title":"Intelligent crack detection in infrastructure using computer vision at the edge","volume":"42","author":"Rizia","year":"2025","journal-title":"Expert Syst."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Li, Y., Ma, C., Li, L., Wang, R., Liu, Z., and Sun, Z. (2024). Lightweight tunnel obstacle detection based on improved YOLOv5. Sensors, 24.","DOI":"10.3390\/s24020395"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"66","DOI":"10.3390\/metrology4010005","article-title":"Deep learning for concrete crack detection and measurement","volume":"4","author":"Nyathi","year":"2024","journal-title":"Metrology"},{"key":"ref_9","unstructured":"Sahoo, A.R., Sahoo, S.S., and Chakraborty, P. (2025). Polyp detection in colonoscopy images using YOLOv11. arXiv."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Kaveh, H., and Alhajj, R. (2024). Recent advances in crack detection technologies for structures: A survey of 2022\u20132023 literature. Front. Built Environ., 10.","DOI":"10.3389\/fbuil.2024.1321634"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"114734","DOI":"10.1016\/j.jobe.2025.114734","article-title":"Advances in crack dataset development and deep learning-based detection models","volume":"116","author":"Song","year":"2025","journal-title":"J. Build. Eng."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"30376","DOI":"10.1038\/s41598-024-81119-1","article-title":"A novel convolutional neural network for enhancing the continuity of pavement crack detection","volume":"14","author":"Zhang","year":"2024","journal-title":"Sci. Rep."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"40073","DOI":"10.1038\/s41598-025-25956-8","article-title":"Efficient crack and surface-type recognition via CNN-block development mechanism and edge profiling","volume":"15","author":"Raza","year":"2025","journal-title":"Sci. Rep."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"121346","DOI":"10.1016\/j.eswa.2023.121346","article-title":"MOD-YOLO: Rethinking the YOLO architecture at the level of feature information and applying it to crack detection","volume":"237","author":"Su","year":"2024","journal-title":"Expert Syst. Appl."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"1743","DOI":"10.1111\/mice.13103","article-title":"A lightweight encoder\u2013decoder network for automatic pavement crack detection","volume":"39","author":"Zhu","year":"2024","journal-title":"Comput.-Aided Civ. Infrastruct. Eng."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Yang, J., Tian, R., Zhou, Z., Tan, X., and He, P. (2025). Flexi-YOLO: A lightweight method for road crack detection in complex environments. PLoS ONE, 20.","DOI":"10.1371\/journal.pone.0325993"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"23167","DOI":"10.1038\/s41598-025-05665-y","article-title":"Building construction crack detection with BCCD YOLO enhanced feature fusion and attention mechanisms","volume":"15","author":"Ren","year":"2025","journal-title":"Sci. Rep."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Zhu, J., Sheng, J., and Cai, Q. (2025). FD2-YOLO: A frequency-domain dual-stream network based on YOLO for crack detection. Sensors, 25.","DOI":"10.3390\/s25113427"},{"key":"#cr-split#-ref_19.1","doi-asserted-by":"crossref","unstructured":"Lin, T.-Y., Doll\u00e1r, P., Girshick, R., He, K., Hariharan, B., and Belongie, S. (2017). Feature pyramid networks for object detection. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition","DOI":"10.1109\/CVPR.2017.106"},{"key":"#cr-split#-ref_19.2","unstructured":"Honolulu, HI, USA, 21-26 July 2017, IEEE."},{"key":"#cr-split#-ref_20.1","doi-asserted-by":"crossref","unstructured":"Dai, J., Qi, H., Xiong, Y., Li, Y., Zhang, G., Hu, H., and Wei, Y. (2017). Deformable convolutional networks. Proceedings of the IEEE International Conference on Computer Vision","DOI":"10.1109\/ICCV.2017.89"},{"key":"#cr-split#-ref_20.2","unstructured":"Venice, Italy, 22-29 October 2017, IEEE."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"38179","DOI":"10.1038\/s41598-025-22092-1","article-title":"Lightweight pavement crack detection model for edge computing devices","volume":"15","author":"Li","year":"2025","journal-title":"Sci. Rep."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Duan, B., Wang, S., Luo, C., and Chen, Z. (2024). Multi-module fusion model for submarine pipeline identification based on YOLOv5. J. Mar. Sci. Eng., 12.","DOI":"10.3390\/jmse12030451"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"e1463","DOI":"10.7717\/peerj-cs.1463","article-title":"CAM-YOLO: Tomato detection and classification based on improved YOLOv5 using combining attention mechanism","volume":"9","author":"Appe","year":"2023","journal-title":"PeerJ Comput. Sci."},{"key":"ref_24","unstructured":"Huang, S., Liu, Q., Chen, C., and Chen, Y. (2025). A real-time concrete crack detection and segmentation model based on YOLOv11. arXiv."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Yin, Y., Zhang, Z., Wei, L., Geng, C., Ran, H., and Zhu, H. (2023). Pedestrian detection algorithm integrating large kernel attention and YOLOV5 lightweight model. PLoS ONE, 18.","DOI":"10.1371\/journal.pone.0294865"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Xing, Y., Han, X., Pan, X., An, D., Liu, W., and Bai, Y. (2024). EMG-YOLO: Road crack detection algorithm for edge computing devices. Front. Neurorobot., 18.","DOI":"10.3389\/fnbot.2024.1423738"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Chen, X., Lian, Q., Chen, X., and Shang, J. (2022). Surface crack detection method for coal rock based on improved YOLOv5. Appl. Sci., 12.","DOI":"10.3390\/app12199695"},{"key":"#cr-split#-ref_28.1","doi-asserted-by":"crossref","unstructured":"Hu, J., Shen, L., and Sun, G. (2018). Squeeze-and-excitation networks. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition","DOI":"10.1109\/CVPR.2018.00745"},{"key":"#cr-split#-ref_28.2","unstructured":"Salt Lake City, UT, USA, 18-23 June 2018, IEEE."},{"key":"#cr-split#-ref_29.1","doi-asserted-by":"crossref","unstructured":"Woo, S., Park, J., Lee, J.-Y., and Kweon, I.S. (2018). CBAM: Convolutional block attention module. Proceedings of the European Conference on Computer Vision","DOI":"10.1007\/978-3-030-01234-2_1"},{"key":"#cr-split#-ref_29.2","unstructured":"Munich, Germany, 8-14 September 2018, Springer."},{"key":"#cr-split#-ref_30.1","doi-asserted-by":"crossref","unstructured":"Hou, Q., Zhou, D., and Feng, J. (2021). Coordinate attention for efficient mobile network design. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition","DOI":"10.1109\/CVPR46437.2021.01350"},{"key":"#cr-split#-ref_30.2","unstructured":"Nashville, TN, USA, 20-25 June 2021, IEEE."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"103514","DOI":"10.1016\/j.autcon.2020.103514","article-title":"A real-time detection approach for bridge cracks based on YOLOv4-FPM","volume":"122","author":"Yu","year":"2021","journal-title":"Autom. Constr."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"4269","DOI":"10.3934\/mbe.2024188","article-title":"Road surface crack detection based on improved YOLOv5s","volume":"21","author":"Ding","year":"2024","journal-title":"Math. Biosci. Eng."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Sagar, N.P., Nagpal, H.S., Chougle, A., Chamola, V., and Sikdar, B. (2023). Computer vision and IoT-enabled robotic platform for automated crack detection in road and bridges. Proceedings of the IEEE 6th International Conference on Multimedia Information Processing and Retrieval (MIPR), IEEE.","DOI":"10.1109\/MIPR59079.2023.00015"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"104745","DOI":"10.1016\/j.autcon.2023.104745","article-title":"Real-time detection of cracks in tiled sidewalks using YOLO-based method applied to unmanned aerial vehicle (UAV) images","volume":"147","author":"Qiu","year":"2023","journal-title":"Autom. Constr."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"105021","DOI":"10.1016\/j.trc.2025.105021","article-title":"Novel pavement crack detection sensor using coordinated mobile robots","volume":"172","author":"Alkhedher","year":"2025","journal-title":"Transp. Res. Part C"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"105783","DOI":"10.1016\/j.autcon.2024.105783","article-title":"Vision-guided robot for automated pixel-level pavement crack sealing","volume":"168","author":"Zhang","year":"2024","journal-title":"Autom. Constr."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"107116","DOI":"10.1016\/j.istruc.2024.107116","article-title":"Automatic crack detection method for high-speed railway box girder based on deep learning techniques and inspection robot","volume":"Volume 68","author":"Guo","year":"2024","journal-title":"Structures"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"24802","DOI":"10.1109\/JSEN.2024.3399008","article-title":"POT-YOLO: Real-time road potholes detection using edge segmentation-based YOLO V8 network","volume":"24","author":"Bhavana","year":"2024","journal-title":"IEEE Sens. J."},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Nguyen, C.L., Nguyen, A., Brown, J., Byrne, T., Ngo, B.T., and Luong, C.X. (2024). Optimising concrete crack detection: A study of transfer learning with application on Nvidia Jetson Nano. Sensors, 24.","DOI":"10.3390\/s24237818"},{"key":"ref_40","first-page":"57","article-title":"Real-time asphalt pothole detection using the YOLOv5 algorithm and the NVIDIA Jetson Nano embedded board","volume":"69","author":"Obreja","year":"2023","journal-title":"Bull. Polytech. Inst. Iasi Electr. Eng. Power Eng. Electron. Sect."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"79247","DOI":"10.1109\/ACCESS.2025.3562612","article-title":"iYOLOV7-TPE-SS: Leveraging improved YOLO model with multilevel hyperparameter optimization for road damage detection on edge devices","volume":"13","author":"Mahmudah","year":"2025","journal-title":"IEEE Access"},{"key":"ref_42","unstructured":"Taffese, W.Z., Sharma, R., Afsharmovahed, M.H., Manogaran, G., and Chen, G. (2025). Benchmarking YOLOv8 for optimal crack detection in civil infrastructure. arXiv."},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Sohaib, M., Arif, M., and Kim, J.M. (2024). Evaluating YOLO models for efficient crack detection in concrete structures using transfer learning. Buildings, 14.","DOI":"10.3390\/buildings14123928"},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Meng, W., and Yuan, Y. (2023). SGN-YOLO: Detecting wood defects with improved YOLOv5 based on semi-global network. Sensors, 23.","DOI":"10.3390\/s23218705"},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"He, Y., Li, C., Li, X., and Bai, T. (2024). A lightweight CNN based on axial depthwise convolution and hybrid attention for remote sensing image dehazing. Remote Sens., 16.","DOI":"10.3390\/rs16152822"},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Ali, M., Javaid, M., Noman, M., Fiaz, M., and Khan, S.H. (2024). Fanet: Feature amplification network for semantic segmentation in cluttered background. Proceedings of the IEEE International Conference on Image Processing (ICIP), IEEE.","DOI":"10.1109\/ICIP51287.2024.10647349"},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"1958","DOI":"10.1109\/TPAMI.2024.3511621","article-title":"Divide-and-conquer: Confluent triple-flow network for RGB-T salient object detection","volume":"47","author":"Tang","year":"2024","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/26\/12\/3892\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,6,26]],"date-time":"2026-06-26T05:09:24Z","timestamp":1782450564000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/26\/12\/3892"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,6,18]]},"references-count":52,"journal-issue":{"issue":"12","published-online":{"date-parts":[[2026,6]]}},"alternative-id":["s26123892"],"URL":"https:\/\/doi.org\/10.3390\/s26123892","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,6,18]]}}}