{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,28]],"date-time":"2026-06-28T23:27:39Z","timestamp":1782689259384,"version":"3.54.5"},"reference-count":72,"publisher":"MDPI AG","issue":"19","license":[{"start":{"date-parts":[[2023,10,3]],"date-time":"2023-10-03T00:00:00Z","timestamp":1696291200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Qingdao Social Science Planning Research Project","award":["2022-389"],"award-info":[{"award-number":["2022-389"]}]},{"name":"Qingdao Social Science Planning Research Project","award":["JJKH20221020KJ"],"award-info":[{"award-number":["JJKH20221020KJ"]}]},{"name":"Scientific Research Project of the Education Department of Jilin Province","award":["2022-389"],"award-info":[{"award-number":["2022-389"]}]},{"name":"Scientific Research Project of the Education Department of Jilin Province","award":["JJKH20221020KJ"],"award-info":[{"award-number":["JJKH20221020KJ"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Road defect detection is a crucial aspect of road maintenance projects, but traditional manual methods are time-consuming, labor-intensive, and lack accuracy. Leveraging deep learning frameworks for object detection offers a promising solution to these challenges. However, the complexity of backgrounds, low resolution, and similarity of cracks make detecting road cracks with high accuracy challenging. To address these issues, a novel road crack detection algorithm, termed Road Defect Detection YOLOv5 (RDD-YOLOv5), was proposed. Firstly, a model was proposed to integrate the transformer structure and explicit vision center to capture the long-distance dependency and aggregate key characteristics. Additionally, the Sigmoid-weighted linear activations in YOLOv5 were replaced with the Gaussian Error Linear Units to enhance the model\u2019s nonlinear fitting capability. To evaluate the algorithm\u2019s performance, a UAV flight platform was constructed, and experimental freebies were provided to boost inspection efficiency. The experimental results demonstrate the effectiveness of RDD-YOLOv5, achieving a mean average precision of 91.48%, surpassing the original YOLOv5 by 2.5%. The proposed model proves its ability to accurately identify road cracks, even under challenging and complex traffic backgrounds. This advancement in road crack detection technology has significant implications for improving road maintenance and safety.<\/jats:p>","DOI":"10.3390\/s23198241","type":"journal-article","created":{"date-parts":[[2023,10,4]],"date-time":"2023-10-04T11:58:57Z","timestamp":1696420737000},"page":"8241","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":39,"title":["RDD-YOLOv5: Road Defect Detection Algorithm with Self-Attention Based on Unmanned Aerial Vehicle Inspection"],"prefix":"10.3390","volume":"23","author":[{"given":"Yutian","family":"Jiang","sequence":"first","affiliation":[{"name":"College of Transportation, Jilin University, Changchun 130022, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Haotian","family":"Yan","sequence":"additional","affiliation":[{"name":"College of Transportation, Jilin University, Changchun 130022, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yiru","family":"Zhang","sequence":"additional","affiliation":[{"name":"College of Communication Engineering, Jilin University, Changchun 130022, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Keqiang","family":"Wu","sequence":"additional","affiliation":[{"name":"College of Communication Engineering, Jilin University, Changchun 130022, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ruiyuan","family":"Liu","sequence":"additional","affiliation":[{"name":"College of Transportation, Jilin University, Changchun 130022, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9098-2666","authenticated-orcid":false,"given":"Ciyun","family":"Lin","sequence":"additional","affiliation":[{"name":"College of Transportation, Jilin University, Changchun 130022, China"},{"name":"Jilin Engineering Research Center for Intelligent Transportation System, Changchun 130022, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2023,10,3]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"21378","DOI":"10.1109\/TITS.2022.3171433","article-title":"CFC-GAN: Forecasting Road Surface Crack Using Forecasted Crack Generative Adversarial Network","volume":"23","author":"Sekar","year":"2022","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"027007","DOI":"10.1117\/1.2172917","article-title":"Wavelet-based pavement distress detection and evaluation","volume":"45","author":"Zhou","year":"2006","journal-title":"Opt. Eng."},{"key":"ref_3","unstructured":"Teschke, K., Nicol, A.M., and Davies, H. (1999). Whole Body Vibration and Back Disorders among Motor Vehicle Drivers and Heavy Equipment Operators: A Review of the Scientific Evidence, University of British Columbia Library."},{"key":"ref_4","unstructured":"Granlund, J., Ahlin, K., and Lundstr\u00f6m, R. (2000). Whole-Body Vibration when Riding on Rough Roads, Swedish National Road Administration."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Silva, N., Shah, V., Soares, J., and Rodrigues, H. (2018). Road anomalies detection system evaluation. Sensors, 18.","DOI":"10.3390\/s18071984"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"1525","DOI":"10.1109\/TITS.2019.2910595","article-title":"Feature pyramid and hierarchical boosting network for pavement crack detection","volume":"21","author":"Yang","year":"2019","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"119397","DOI":"10.1016\/j.conbuildmat.2020.119397","article-title":"A cost effective solution for pavement crack inspection using cameras and deep neural networks","volume":"256","author":"Mei","year":"2020","journal-title":"Constr. Build. Mater."},{"key":"ref_8","first-page":"121","article-title":"Review of pavement detection technology","volume":"17","author":"Ma","year":"2017","journal-title":"J. Traffic Transp. Engineering"},{"key":"ref_9","unstructured":"Kim, J.Y. (2008). Development of New Automated Crack Measurement Algorithm Using Laser Images of Pavement Surface, The University of Iowa."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"154","DOI":"10.1061\/(ASCE)1076-0342(2005)11:3(154)","article-title":"Real-time automated survey system of pavement cracking in parallel environment","volume":"11","author":"Wang","year":"2005","journal-title":"J. Infrastruct. Syst."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"013017","DOI":"10.1117\/1.2177650","article-title":"Automatic inspection of pavement cracking distress","volume":"15","author":"Huang","year":"2006","journal-title":"J. Electron. Imaging"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"441","DOI":"10.1007\/s42835-019-00230-w","article-title":"Distribution line pole detection and counting based on YOLO using UAV inspection line video","volume":"15","author":"Chen","year":"2020","journal-title":"J. Electr. Eng. Technol."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Hassan, S.-A., Rahim, T., and Shin, S.-Y. (2021). An Improved Deep Convolutional Neural Network-Based Autonomous Road Inspection Scheme Using Unmanned Aerial Vehicles. Electronics, 10.","DOI":"10.3390\/electronics10222764"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Rivas, A., Chamoso, P., Gonz\u00e1lez-Briones, A., and Corchado, J.M. (2018). Detection of cattle using drones and convolutional neural networks. Sensors, 18.","DOI":"10.3390\/s18072048"},{"key":"ref_15","first-page":"100250","article-title":"UAV based wilt detection system via convolutional neural networks","volume":"28","author":"Dang","year":"2020","journal-title":"Sustain. Comput. Inform. Syst."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"103991","DOI":"10.1016\/j.autcon.2021.103991","article-title":"Pavement distress detection using convolutional neural networks with images captured via UAV","volume":"133","author":"Zhu","year":"2022","journal-title":"Autom. Constr."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Salman, M., Mathavan, S., Kamal, K., and Rahman, M. (2013, January 6\u20139). Pavement crack detection using the Gabor filter. Proceedings of the 16th International IEEE Conference on Intelligent Transportation Systems (ITSC 2013), The Hague, The Netherlands.","DOI":"10.1109\/ITSC.2013.6728529"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Abdellatif, M., Peel, H., Cohn, A.G., and Fuentes, R. (2020). Pavement crack detection from hyperspectral images using a novel asphalt crack index. Remote Sens., 12.","DOI":"10.3390\/rs12183084"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"140","DOI":"10.13176\/11.167","article-title":"A Local Binary Pattern Based Methods for Pavement Crack Detection","volume":"5","author":"Yong","year":"2010","journal-title":"J. Pattern Recognit. Res."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"244","DOI":"10.1111\/j.1467-8667.2011.00736.x","article-title":"Automatic road defect detection by textural pattern recognition based on AdaBoost","volume":"27","author":"Cord","year":"2012","journal-title":"Comput. Aided Civ. Infrastruct. Eng."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"651","DOI":"10.1049\/ell2.12562","article-title":"AugMoCrack: Augmented morphological attention network for weakly supervised crack detection","volume":"58","author":"Hong","year":"2022","journal-title":"Electron. Lett."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"672","DOI":"10.1177\/14759217221089571","article-title":"Feature pyramid network with self-guided attention refinement module for crack segmentation","volume":"22","author":"Ong","year":"2023","journal-title":"Struct. Health Monit."},{"key":"ref_23","unstructured":"Singh, J., and Shekhar, S. (2018). Road damage detection and classification in smartphone captured images using mask r-cnn. arXiv."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"107133","DOI":"10.1016\/j.dib.2021.107133","article-title":"RDD2020: An annotated image dataset for automatic road damage detection using deep learning","volume":"36","author":"Arya","year":"2021","journal-title":"Data Brief"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"1659","DOI":"10.1080\/10298436.2020.1714047","article-title":"Pavement distress detection and classification based on YOLO network","volume":"22","author":"Du","year":"2021","journal-title":"Int. J. Pavement Eng."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Mao, Z., Zhao, C., Zheng, Y., Mao, Y., Li, H., Hua, L., and Liu, Y. (2020, January 21\u201323). Research on detection method of pavement diseases based on Unmanned Aerial Vehicle (UAV). Proceedings of the 2020 International Conference on Image, Video Processing and Artificial Intelligence, Shanghai, China.","DOI":"10.1117\/12.2580285"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Wu, C., Ye, M., Zhang, J., and Ma, Y. (2023). YOLO-LWNet: A lightweight road damage object detection network for mobile terminal devices. Sensors, 23.","DOI":"10.3390\/s23063268"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Quan, Y., Zhang, D., Zhang, L., and Tang, J. (2022). Centralized Feature Pyramid for Object Detection. arXiv.","DOI":"10.1109\/TIP.2023.3297408"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Guo, Z., Wang, C., Yang, G., Huang, Z., and Li, G. (2022). Msft-yolo: Improved yolov5 based on transformer for detecting defects of steel surface. Sensors, 22.","DOI":"10.3390\/s22093467"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Liu, Z., Lin, Y., Cao, Y., Hu, H., Wei, Y., Zhang, Z., Lin, S., and Guo, B. (2021, January 11\u201317). Swin transformer: Hierarchical vision transformer using shifted windows. Proceedings of the IEEE\/CVF International Conference on Computer Vision, 2021, Montreal, BC, Canada.","DOI":"10.1109\/ICCV48922.2021.00986"},{"key":"ref_31","unstructured":"Hendrycks, D., and Gimpel, K. (2016). Gaussian Error Linear Units (GELUs). arXiv."},{"key":"ref_32","unstructured":"Glorot, X., Bordes, A., and Bengio, Y. (2011, January 11\u201313). Deep sparse rectifier neural networks. Proceedings of the Fourteenth International Conference on Artificial Intelligence And Statistics, 2011, Fort Lauderdale, FL, USA."},{"key":"ref_33","unstructured":"Clevert, D.-A., Unterthiner, T., and Hochreiter, S. (2015). Fast and accurate deep network learning by exponential linear units (elus). arXiv."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"533","DOI":"10.24425\/123905","article-title":"Economical methods for measuring road surface roughness","volume":"25","author":"Grabowski","year":"2018","journal-title":"Metrol. Meas. Syst."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"9628","DOI":"10.3390\/s111009628","article-title":"Adaptive road crack detection system by pavement classification","volume":"11","author":"Gavilan","year":"2011","journal-title":"Sensors"},{"key":"ref_36","unstructured":"Luo, R. (2017). Research of Pavement Crack Detection Algorithm Based on Image Processing, Anhui Polytechnic University."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"51","DOI":"10.1007\/BF03325740","article-title":"Elements of automated survey of pavements and a 3D methodology","volume":"19","author":"Wang","year":"2011","journal-title":"J. Mod. Transp."},{"key":"ref_38","unstructured":"Mejias, L., Campoy, P., Saripalli, S., and Sukhatme, G.S. (2015, January 26\u201330). A visual servoing approach for tracking features in urban areas using an autonomous helicopter. Proceedings of the IEEE International Conference on Robotics & Automation, 2015, Seattle, WA, USA."},{"key":"ref_39","unstructured":"Chen, J., Geng, S., Yan, Y., Huang, D., Liu, H., and Li, Y. (2021). Vehicle Re-identification Method Based on Vehicle Attribute and Mutual Exclusion Between Cameras. arXiv."},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Lee, J.-H., Yoon, S.-S., Kim, I.-H., and Jung, H.-J. (2018, January 5\u20138). Diagnosis of crack damage on structures based on image processing techniques and R-CNN using unmanned aerial vehicle (UAV). Proceedings of the Sensors and Smart Structures Technologies for Civil, Mechanical, and Aerospace Systems 2018, Denver, CO, USA.","DOI":"10.1117\/12.2296691"},{"key":"ref_41","unstructured":"Jin, Z. (2022). Research on Highway Inspection System Based on UAV Autonomous Flight, Wuhan Textile University."},{"key":"ref_42","first-page":"599","article-title":"Detection method for road pavement defect of UAV imagery based on computer vision","volume":"35","author":"Joo","year":"2017","journal-title":"J. Korean Soc. Surv. Geod. Photogramm. Cartogr."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"1885","DOI":"10.1016\/j.cor.2005.07.019","article-title":"Learning multicriteria fuzzy classification method PROAFTN from data","volume":"34","author":"Belacel","year":"2007","journal-title":"Comput. Oper. Res."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"6630802","DOI":"10.1155\/2021\/6630802","article-title":"A Method of Surface Defect Detection of Irregular Industrial","volume":"2021","author":"Li","year":"2021","journal-title":"Wirel. Commun. Mob. Comput."},{"key":"ref_45","unstructured":"Oliveira, H., and Correia, P.L. (September, January 28). Road Surface Crack Detection: Improved Segmentation with Pixel-based Refinement. Proceedings of the 25th European Signal Processing Conference (EUSIPCO), Kos, Greece."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"3434","DOI":"10.1109\/TITS.2016.2552248","article-title":"Automatic Road Crack Detection Using Random Structured Forests","volume":"17","author":"Shi","year":"2016","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"1604130","DOI":"10.1155\/2017\/1604130","article-title":"Asphalt pavement pothole detection and segmentation based on wavelet energy field","volume":"2017","author":"Wang","year":"2017","journal-title":"Math. Probl. Eng."},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Oliveira, H., and Correia, P.L. (2008, January 25\u201329). Supervised strategies for cracks detection in images of road pavement flexible surfaces. Proceedings of the European Signal Processing Conference, 2008, Lausanne, Switzerland.","DOI":"10.5772\/7448"},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"227","DOI":"10.1016\/j.patrec.2011.11.004","article-title":"Crack Tree: Automatic crack detection from pavement images","volume":"33","author":"Cao","year":"2012","journal-title":"Pattern Recogn. Lett."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"2718","DOI":"10.1109\/TITS.2015.2477675","article-title":"Automatic crack detection on two-dimensional pavement images: An algorithm based on minimal path selection","volume":"17","author":"Amhaz","year":"2016","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"5012311","DOI":"10.1109\/TIM.2021.3092510","article-title":"FPCB Surface Defect Detection: A Decoupled Two-Stage Object Detection Framework","volume":"70","author":"Luo","year":"2021","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"ref_52","doi-asserted-by":"crossref","unstructured":"Zhang, S., Wen, L., Bian, X., Lei, Z., and Li, S.Z. (2018, January 18\u201323). Single-Shot Refinement Neural Network for Object Detection. Proceedings of the 2018 IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00442"},{"key":"ref_53","doi-asserted-by":"crossref","unstructured":"Girshick, R., Donahue, J., Darrell, T., and Malik, J. (2014, January 23\u201328). Rich feature hierarchies for accurate object detection and semantic segmentation. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2014, Columbus, OH, USA.","DOI":"10.1109\/CVPR.2014.81"},{"key":"ref_54","doi-asserted-by":"crossref","unstructured":"Girshick, R. (2015, January 11\u201318). Fast R-CNN. Proceedings of the IEEE International Conference on Computer Vision (ICCV), Santiago, Chile.","DOI":"10.1109\/ICCV.2015.169"},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"1137","DOI":"10.1109\/TPAMI.2016.2577031","article-title":"Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks","volume":"39","author":"Ren","year":"2017","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"012008","DOI":"10.1088\/1742-6596\/1903\/1\/012008","article-title":"Improvements of YoloV3 for road damage detection","volume":"1903","author":"Wang","year":"2021","journal-title":"J. Phys. Conf. Ser."},{"key":"ref_57","unstructured":"Naseer, M., Ranasinghe, K., Khan, S., Hayat, M., Shahbaz Khan, F., and Yang, M.-H. (2021). Intriguing Properties of Vision Transformers. arXiv."},{"key":"ref_58","doi-asserted-by":"crossref","unstructured":"Zhu, X., Lyu, S., Wang, X., and Zhao, Q. (2021, January 11\u201317). TPH-YOLOv5: Improved YOLOv5 Based on Transformer Prediction Head for Object Detection on Drone-captured Scenarios. Proceedings of the IEEE\/CVF International Conference on Computer Vision 2021, Montreal, BC, Canada.","DOI":"10.1109\/ICCVW54120.2021.00312"},{"key":"ref_59","doi-asserted-by":"crossref","unstructured":"Woo, S., Park, J., Lee, J.-Y., and Kweon, I.S. (2018, January 8\u201314). Cbam: Convolutional block attention module. Proceedings of the European Conference on Computer Vision (ECCV), 2018, Munich, Germany.","DOI":"10.1007\/978-3-030-01234-2_1"},{"key":"ref_60","doi-asserted-by":"crossref","unstructured":"Jo, H., Na, Y.-H., and Song, J.-B. (2017, January 18\u201321). Data augmentation using synthesized images for object detection. Proceedings of the 2017 17th International Conference on Control, Automation and Systems (ICCAS), 2017, Jeju, Republic of Korea.","DOI":"10.23919\/ICCAS.2017.8204369"},{"key":"ref_61","doi-asserted-by":"crossref","unstructured":"Zhang, H., Cisse, M., Dauphin, Y.N., and Lopez-Paz, D. (2017). mixup: Beyond empirical risk minimization. arXiv.","DOI":"10.1007\/978-1-4899-7687-1_79"},{"key":"ref_62","doi-asserted-by":"crossref","unstructured":"Yun, S., Han, D., Oh, S.J., Chun, S., Choe, J., and Yoo, Y. (November, January 27). Cutmix: Regularization strategy to train strong classifiers with localizable features. Proceedings of the IEEE\/CVF International Conference On Computer Vision, 2019, Seoul, Republic of Korea.","DOI":"10.1109\/ICCV.2019.00612"},{"key":"ref_63","unstructured":"Harris, E., Marcu, A., Painter, M., Niranjan, M., Pr\u00fcgel-Bennett, A., and Hare, J. (2020). Fmix: Enhancing mixed sample data augmentation. arXiv."},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1016\/j.neunet.2017.12.012","article-title":"Sigmoid-weighted linear units for neural network function approximation in reinforcement learning","volume":"107","author":"Elfwing","year":"2018","journal-title":"Neural Netw."},{"key":"ref_65","doi-asserted-by":"crossref","unstructured":"He, K., Gkioxari, G., Doll\u00e1r, P., and Girshick, R. (2017, January 22\u201329). Mask r-cnn. Proceedings of the IEEE International Conference On Computer Vision, 2017, Venice, Italy.","DOI":"10.1109\/ICCV.2017.322"},{"key":"ref_66","unstructured":"Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., and Houlsby, N. (2020). An Image is Worth 16\u00d716 Words: Transformers for Image Recognition at Scale. arXiv."},{"key":"ref_67","doi-asserted-by":"crossref","first-page":"342","DOI":"10.1111\/mice.12042","article-title":"Road crack detection using visual features extracted by Gabor filters","volume":"29","author":"Zalama","year":"2014","journal-title":"Comput. Aided Civ. Infrastruct. Eng."},{"key":"ref_68","unstructured":"Redmon, J., and Farhadi, A. (2018). YOLOv3: An Incremental Improvement. arXiv."},{"key":"ref_69","unstructured":"Bochkovskiy, A., Wang, C.-Y., and Liao, H.-Y.M. (2020). YOLOv4: Optimal Speed and Accuracy of Object Detection. arXiv."},{"key":"ref_70","doi-asserted-by":"crossref","unstructured":"Wang, C.-Y., Bochkovskiy, A., and Liao, H.-Y.M. (2022). YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors. arXiv.","DOI":"10.1109\/CVPR52729.2023.00721"},{"key":"ref_71","doi-asserted-by":"crossref","first-page":"1127","DOI":"10.1111\/mice.12387","article-title":"Road damage detection and classification using deep neural networks with smartphone images","volume":"33","author":"Maeda","year":"2018","journal-title":"Comput.-Aided Civ. Infrastruct. Eng."},{"key":"ref_72","doi-asserted-by":"crossref","unstructured":"Arya, D., Maeda, H., Ghosh, S.K., Toshniwal, D., and Sekimoto, Y. (2022). Rdd2022: A multi-national image dataset for automatic road damage detection. arXiv.","DOI":"10.1016\/j.dib.2021.107133"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/19\/8241\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T21:04:50Z","timestamp":1760130290000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/19\/8241"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,10,3]]},"references-count":72,"journal-issue":{"issue":"19","published-online":{"date-parts":[[2023,10]]}},"alternative-id":["s23198241"],"URL":"https:\/\/doi.org\/10.3390\/s23198241","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,10,3]]}}}