{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,5]],"date-time":"2026-10-05T00:52:09Z","timestamp":1791161529892,"version":"4.1.0"},"reference-count":70,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2026,7,5]],"date-time":"2026-07-05T00:00:00Z","timestamp":1783209600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"},{"start":{"date-parts":[[2026,10,5]],"date-time":"2026-10-05T00:00:00Z","timestamp":1791158400000},"content-version":"vor","delay-in-days":92,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Sci Rep"],"DOI":"10.1038\/s41598-026-59135-0","type":"journal-article","created":{"date-parts":[[2026,7,5]],"date-time":"2026-07-05T14:04:18Z","timestamp":1783260258000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Detection of underground objects from GPR data using a lightweight YOLO-based approach"],"prefix":"10.1038","volume":"16","author":[{"given":"Muhammed M\u00fccahit","family":"Arvas","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ahmet","family":"\u00c7\u0131nar","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,7,5]]},"reference":[{"issue":"1","key":"59135_CR1","first-page":"167","volume":"1","author":"A Tronics","year":"2019","unstructured":"Tronics, A. & Bahder, I. Penggunaan aplikasi GPR (ground penetrating radar) dengan metode non-destructive untuk kolektifitas data kualitatif pada analisa subsurface tanah ekstrim lunak. Prosiding Temu Profesi Tahunan PERHAPI. 1 (1), 167\u2013180 (2019).","journal-title":"Prosiding Temu Profesi Tahunan PERHAPI"},{"key":"59135_CR2","doi-asserted-by":"crossref","unstructured":"Barone, P. M., Ferrara, C., Pettinelli, E. & Fazzari, A. Forensic geophysics: How the GPR technique can help with forensic investigations. In Soil in Criminal and Environmental Forensics: Proceedings of the Soil Forensics Special, 6th European Academy of Forensic Science Conference, The Hague. 213\u2013227. (Springer, 2016).","DOI":"10.1007\/978-3-319-33115-7_14"},{"key":"59135_CR3","doi-asserted-by":"crossref","unstructured":"Catapano, I., Gennarelli, G., Ludeno, G., Soldovieri, F. & Persico, R. Ground-penetrating radar: Operation principle and data processing. Wiley Encycl. Electr. Electron. Eng. 1\u201323 (1999).","DOI":"10.1002\/047134608X.W8383"},{"issue":"3","key":"59135_CR4","doi-asserted-by":"publisher","first-page":"20","DOI":"10.1109\/67.849021","volume":"13","author":"R Bernstein","year":"2000","unstructured":"Bernstein, R., Oristaglio, M., Miller, D. E. & Haldorsen, J. Imaging radar maps underground objects in 3-D. IEEE Comput. Appl. Power. 13 (3), 20\u201324 (2000).","journal-title":"IEEE Comput. Appl. Power"},{"issue":"(13","key":"59135_CR5","doi-asserted-by":"publisher","first-page":"3033","DOI":"10.3390\/rs14133033","volume":"14","author":"F Lombardi","year":"2022","unstructured":"Lombardi, F., Podd, F. & Solla, M. From its core to the niche: Insights from GPR applications. Remote Sens. ; 14 (13): 3033. (2022).","journal-title":"Remote Sens."},{"issue":"9","key":"59135_CR6","doi-asserted-by":"publisher","first-page":"1417","DOI":"10.3390\/rs10091417","volume":"10","author":"B Park","year":"2018","unstructured":"Park, B., Kim, J., Lee, J., Kang, M. S. & An, Y. K. Underground object classification for urban roads using instantaneous phase analysis of ground-penetrating radar (GPR) data. Remote Sens. 10 (9), 1417 (2018).","journal-title":"Remote Sens."},{"key":"59135_CR7","doi-asserted-by":"crossref","unstructured":"Barone, P. M. & Ferrara, C. The past beneath the present: GPR as a scientific investigation for archaeology and cultural heritage preservation. Int. J. Conserv. Sci. 8(4) (2017).","DOI":"10.1117\/12.2241872"},{"issue":"5","key":"59135_CR8","first-page":"832","volume":"20","author":"S G\u00fcney","year":"2020","unstructured":"G\u00fcney, S. & \u00c7olak, D. Hedef tespiti i\u00e7in yere n\u00fcfuz eden radar verisinde \u00f6n i\u015fleme algoritmalar\u0131n\u0131n kar\u015f\u0131la\u015ft\u0131r\u0131lmas\u0131. Afyon Kocatepe \u00dcniv. Fen Ve M\u00fchendislik Bilimleri Dergisi. 20 (5), 832\u2013843 (2020).","journal-title":"Afyon Kocatepe \u00dcniv. Fen Ve M\u00fchendislik Bilimleri Dergisi"},{"key":"59135_CR9","doi-asserted-by":"crossref","unstructured":"Kruger, H. & Ewald, H. Handheld metal detector with online visualisation and classification for the humanitarian mine clearance. In Sensors, 2008. 415\u2013418 (IEEE, 2008).","DOI":"10.1109\/ICSENS.2008.4716466"},{"key":"59135_CR10","first-page":"110882","volume":"325","author":"TB Kelly","year":"2021","unstructured":"Kelly, T. B. et al. A novel approach to 3D modelling ground-penetrating radar (GPR) data\u2013A case study of a cemetery and applications for criminal investigation. For. Sci. Int. 325, 110882 (2021).","journal-title":"For. Sci. Int."},{"issue":"4","key":"59135_CR11","first-page":"805","volume":"8","author":"G K\u0131l\u0131\u00e7","year":"2022","unstructured":"K\u0131l\u0131\u00e7, G. Yer radar\u0131 (GPR) uygulamas\u0131 ile k\u0131smi y\u0131k\u0131lm\u0131\u015f bir k\u00f6pr\u00fcn\u00fcn sa\u011fl\u0131k durumunun belirlenmesi. J. Adv. Res. Nat. Appl. Sci. 8 (4), 805\u2013819 (2022).","journal-title":"J. Adv. Res. Nat. Appl. Sci."},{"issue":"17","key":"59135_CR12","doi-asserted-by":"publisher","first-page":"3494","DOI":"10.3390\/rs13173494","volume":"13","author":"N Iftimie","year":"2021","unstructured":"Iftimie, N., Savin, A., Steigmann, R. & Dobrescu, G. S. Underground pipeline identification into a non-destructive case study based on ground-penetrating radar imaging. Remote Sens. 13 (17), 3494 (2021).","journal-title":"Remote Sens."},{"key":"59135_CR13","unstructured":"Savin, A. et al. Increasing the probability of detection and evaluation of buried metallic objects by data fusion GPR-low frequency electromagnetic sensor array. In 4th European-American Workshop on Reliability of NDE. 1\u20138 (2009)."},{"key":"59135_CR14","doi-asserted-by":"crossref","unstructured":"Halabe, U. B., Agrawal, S., Gopalakrishnan, B. & Grushecky, S. Defect detection in wooden logs using ground penetrating radar. In AIP Conference Proceedings. Vol. 894(1). 1368\u20131375. (American Institute of Physics, 2007).","DOI":"10.1063\/1.2718125"},{"issue":"2","key":"59135_CR15","doi-asserted-by":"publisher","first-page":"235","DOI":"10.46387\/bjesr.994785","volume":"3","author":"H K\u00fct\u00fckc\u00fco\u011flugil","year":"2021","unstructured":"K\u00fct\u00fckc\u00fco\u011flugil, H., Ko\u015faro\u011flu, S. & Bilim, F. Su borusu hatlar\u0131nda s\u0131z\u0131nt\u0131 tespitinde yer radari y\u00f6ntemi uygulamas\u0131 (GPR). M\u00fchendislik Bilimleri ve Ara\u015ft\u0131rmalar\u0131 Dergisi. 3 (2), 235\u2013244 (2021).","journal-title":"M\u00fchendislik Bilimleri ve Ara\u015ft\u0131rmalar\u0131 Dergisi"},{"key":"59135_CR16","doi-asserted-by":"crossref","unstructured":"Alshamy, H. M. et al. Evaluation of GPR Detection for buried objects material with different depths and scanning angles. In IOP Conference Series: Materials Science and Engineering. Vol. 1090(1). 012042. (IOP Publishing, 2021).","DOI":"10.1088\/1757-899X\/1090\/1\/012042"},{"key":"59135_CR17","doi-asserted-by":"publisher","first-page":"339","DOI":"10.2528\/PIERB12120508","volume":"49","author":"F Soldovieri","year":"2013","unstructured":"Soldovieri, F. et al. GPR estimation of the geometrical features of buried metallic targets in testing conditions. Prog. Electromagnet. Res. B. 49, 339\u2013362 (2013).","journal-title":"Prog. Electromagnet. Res. B"},{"issue":"1","key":"59135_CR18","first-page":"71","volume":"10","author":"R Akdo\u011fan","year":"2021","unstructured":"Akdo\u011fan, R., Dilaver, F. K., Durukan, T. & Altuncu, Y. Yer alt\u0131na g\u00f6m\u00fcl\u00fc \u00fc\u00e7 boyutlu cisimlerin bozulmu\u015f born iteratif y\u00f6ntemi ile tespit edilmesi. Ni\u011fde \u00d6mer Halisdemir \u00dcniv. M\u00fchendislik Bilimleri Dergisi 10 (1), 71\u201378 (2021).","journal-title":"Ni\u011fde \u00d6mer Halisdemir \u00dcniv. M\u00fchendislik Bilimleri Dergisi"},{"issue":"1","key":"59135_CR19","doi-asserted-by":"publisher","first-page":"21","DOI":"10.59314\/tujes.1464575","volume":"25","author":"S Yazar","year":"2023","unstructured":"Yazar, S., Ta\u015fk\u0131n, D. & Bahar, E. Efficient soil moisture monitoring without \u0131n-situ probes: LSTM-based bluetooth signal strengths analysis. Trakya \u00dcniv. M\u00fchendislik Bilimleri Dergisi. 25 (1), 21\u201338 (2023).","journal-title":"Trakya \u00dcniv. M\u00fchendislik Bilimleri Dergisi"},{"issue":"6","key":"59135_CR20","doi-asserted-by":"publisher","first-page":"1011","DOI":"10.3390\/rs16061011","volume":"16","author":"B Arendt","year":"2024","unstructured":"Arendt, B., Schneider, M., Mayer, W. & Walter, T. Environmental \u0131nfluences on the detection of buried objects with a ground-penetrating radar. Remote Sens. 16 (6), 1011 (2024).","journal-title":"Remote Sens."},{"key":"59135_CR21","doi-asserted-by":"crossref","unstructured":"Alzeyadi, A., Hu, J. & Yu, T. Electromagnetic sensing of a subsurface metallic object at different depths. In Nondestructive Characterization and Monitoring of Advanced Materials, Aerospace, Civil Infrastructure, and Transportation XIII. Vol. 10971. 21\u201331. (SPIE, 2019).","DOI":"10.1117\/12.2514460"},{"key":"59135_CR22","doi-asserted-by":"crossref","unstructured":"Zhang, J. & Nath, B. Processing and analysis of ground penetrating radar landmine detection. In International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems. 1204\u20131209. (Springer, 2004).","DOI":"10.1007\/978-3-540-24677-0_123"},{"issue":"1","key":"59135_CR23","doi-asserted-by":"publisher","first-page":"51","DOI":"10.1109\/TGRS.2016.2592679","volume":"55","author":"Q Dou","year":"2016","unstructured":"Dou, Q., Wei, L., Magee, D. R. & Cohn, A. G. Real-time hyperbola recognition and fitting in GPR data. IEEE Trans. Geosci. Remote Sens. 55 (1), 51\u201362 (2016).","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"59135_CR24","doi-asserted-by":"crossref","unstructured":"Ali, H. et al. A cascade hyperbolic recognition of buried objects using hybrid feature extraction in ground penetrating radar images.  J. Phys. Conf. Ser.  1997(1),  012018. (IOP Publishing, 2021).","DOI":"10.1088\/1742-6596\/1997\/1\/012018"},{"issue":"15","key":"59135_CR25","doi-asserted-by":"publisher","first-page":"3665","DOI":"10.3390\/rs14153665","volume":"14","author":"T Wunderlich","year":"2022","unstructured":"Wunderlich, T., Wilken, D., Majchczack, B. S., Segschneider, M. & Rabbel, W. Hyperbola detection with RetinaNet and comparison of hyperbola fitting methods in GPR data from an archaeological site. Remote Sens. 14 (15), 3665 (2022).","journal-title":"Remote Sens."},{"key":"59135_CR26","doi-asserted-by":"crossref","unstructured":"Bai, X. et al. A comprehensive review of conventional and deep learning approaches for ground-penetrating radar detection of raw data. Appl. Sci. 13(13), 7992  (2023).","DOI":"10.3390\/app13137992"},{"issue":"4","key":"59135_CR27","doi-asserted-by":"publisher","first-page":"04023040","DOI":"10.1061\/JPSEA2.PSENG-1444","volume":"14","author":"LCM Amaral","year":"2023","unstructured":"Amaral, L. C. M., Roshan, A. & Bayat, A. Automatic detection and classification of underground objects in ground penetrating radar \u0131mages using machine learning. J. Pipeline Syst. Eng. Pract. 14 (4), 04023040 (2023).","journal-title":"J. Pipeline Syst. Eng. Pract."},{"key":"59135_CR28","doi-asserted-by":"crossref","unstructured":"Li, T., Xue, W., Yang, X. & Wu, D.  Road underground target detection in ground penetrating radar \u0131mages based on YOLO models. In 2023 China Automation Congress (CAC). 2837\u20132842. (IEEE, 2023).","DOI":"10.1109\/CAC59555.2023.10450646"},{"issue":"22","key":"59135_CR29","doi-asserted-by":"publisher","first-page":"6476","DOI":"10.3390\/s20226476","volume":"20","author":"Y Li","year":"2020","unstructured":"Li, Y., Zhao, Z., Luo, Y. & Qiu, Z. Real-time pattern-recognition of GPR images with YOLO v3 implemented by tensorflow. Sensors 20 (22), 6476 (2020).","journal-title":"Sensors"},{"key":"59135_CR30","doi-asserted-by":"crossref","unstructured":"Redmon, J. You only look once: Unified, real-time object detection. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. (2016).","DOI":"10.1109\/CVPR.2016.91"},{"key":"59135_CR31","first-page":"e02779","volume":"20","author":"C Liu","year":"2024","unstructured":"Liu, C., Yao, Y., Li, J., Qian, J. & Liu, L. Research on lightweight GPR road surface disease image recognition and data expansion algorithm based on YOLO and GAN. Case Stud. Constr. Mater. 20, e02779 (2024).","journal-title":"Case Stud. Constr. Mater."},{"issue":"5","key":"59135_CR32","doi-asserted-by":"publisher","first-page":"830","DOI":"10.3390\/electronics13050830","volume":"13","author":"Y Yang","year":"2024","unstructured":"Yang, Y., Huang, L., Zhang, Z., Zhang, J. & Zhao, G. CycleGAN-based data augmentation for subgrade disease detection in GPR images with YOLOv5. Electronics 13 (5), 830 (2024).","journal-title":"Electronics"},{"key":"59135_CR33","doi-asserted-by":"publisher","first-page":"105506","DOI":"10.1016\/j.autcon.2024.105506","volume":"165","author":"TX Luo","year":"2024","unstructured":"Luo, T. X., Zhou, Y., Zheng, Q., Hou, F. & Lin, C. Lightweight deep learning model for identifying tunnel lining defects based on GPR data. Autom. Constr. 165, 105506 (2024).","journal-title":"Autom. Constr."},{"key":"59135_CR34","doi-asserted-by":"crossref","unstructured":"Ma, Y., Lei, W., Pang, Z., Zheng, Z. & Tan, X. Rebar clutter suppression and road defects localization in GPR B-scan \u0131mages based on SuppRebar-GAN and EC-Yolov7 networks. IEEE Trans. Geosci. Remote Sens. (2024).","DOI":"10.1109\/TGRS.2024.3373025"},{"key":"59135_CR35","doi-asserted-by":"publisher","first-page":"105185","DOI":"10.1016\/j.autcon.2023.105185","volume":"158","author":"C Liu","year":"2024","unstructured":"Liu, C. et al. Advances in automatic identification of road subsurface distress using ground penetrating radar: State of the art and future trends. Autom. Constr. 158, 105185 (2024).","journal-title":"Autom. Constr."},{"key":"59135_CR36","doi-asserted-by":"publisher","first-page":"103087","DOI":"10.1016\/j.ndteint.2024.103087","volume":"144","author":"L Zhao","year":"2024","unstructured":"Zhao, L., Xu, Q., Song, Z., Meng, S. & Liu, S. Dynamic wave tunnel lining GPR images multi-disease detection method based on deep learning. NDT E Int. 144, 103087 (2024).","journal-title":"NDT E Int."},{"issue":"6","key":"59135_CR37","first-page":"2455","volume":"67","author":"Z LIU","year":"2024","unstructured":"LIU, Z., GU, X., LI, J., JIANG, J. & DONG, Q., & Deep learning-enhanced numerical simulation of ground penetrating radar and image detection of road cracks. Chin. J. Geophys. 67 (6), 2455\u20132471 (2024).","journal-title":"Chin. J. Geophys."},{"key":"59135_CR38","doi-asserted-by":"crossref","unstructured":"Sonoda, J. & Nakam\u0131ch\u0131, K. A simple augmentation method using cutout for ground penetrating radar \u0131mage in deep learning. In IEICE Transactions on Electronics. 2023ESS0005 (2024).","DOI":"10.1587\/transele.2023ESS0005"},{"key":"59135_CR39","doi-asserted-by":"crossref","unstructured":"Wang, N., Zhang, Z., Hu, H., Li, B. & Lei, J. Underground Defects Detection Based on GPR by Fusing Simple Linear \u0130terative Clustering Phash (SLIC-Phash) and Convolutional Block Attention Module (CBAM)-YOLOv8. (IEEE Access, 2024).","DOI":"10.1109\/ACCESS.2024.3365959"},{"issue":"6","key":"59135_CR40","doi-asserted-by":"publisher","first-page":"18309","DOI":"10.1007\/s11042-023-15797-w","volume":"83","author":"GF Angelis","year":"2024","unstructured":"Angelis, G. F. et al. AI-enabled underground water pipe non-destructive \u0131nspection. Multimed. Tools Appl. 83 (6), 18309\u201318332 (2024).","journal-title":"Multimed. Tools Appl."},{"key":"59135_CR41","doi-asserted-by":"crossref","unstructured":"Li, F. et al. Research on 3D Ground Penetrating Radar Deep Underground Cavity \u0130dentification Algorithm in Urban Roads Using Multi-Dimensional Time-Frequency Features. Vol. 143.  103060 (NDT & E International, 2024).","DOI":"10.1016\/j.ndteint.2024.103060"},{"issue":"1","key":"59135_CR42","doi-asserted-by":"publisher","first-page":"41","DOI":"10.1007\/s13349-023-00681-w","volume":"14","author":"Z Situ","year":"2024","unstructured":"Situ, Z., Teng, S., Liao, X., Chen, G. & Zhou, Q. Real-time sewer defect detection based on YOLO network, transfer learning, and channel pruning algorithm. J. Civ. Struct. Health Monit. 14 (1), 41\u201357 (2024).","journal-title":"J. Civ. Struct. Health Monit."},{"issue":"6","key":"59135_CR43","doi-asserted-by":"publisher","first-page":"1333","DOI":"10.1007\/s13349-023-00684-7","volume":"13","author":"Y Li","year":"2023","unstructured":"Li, Y. & Bao, T. A real-time multi-defect automatic identification framework for concrete dams via improved YOLOv5 and knowledge distillation. J. Civ. Struct. Health Monit. 13 (6), 1333\u20131349 (2023).","journal-title":"J. Civ. Struct. Health Monit."},{"issue":"17","key":"59135_CR44","doi-asserted-by":"publisher","first-page":"2472","DOI":"10.1111\/mice.13070","volume":"38","author":"PJ Chun","year":"2023","unstructured":"Chun, P. J., Suzuki, M. & Kato, Y. Iterative application of generative adversarial networks for improved buried pipe detection from images obtained by ground-penetrating radar. Comput. Aid. Civ. Infrastruct. Eng. 38 (17), 2472\u20132490 (2023).","journal-title":"Comput. Aid. Civ. Infrastruct. Eng."},{"key":"59135_CR45","doi-asserted-by":"crossref","unstructured":"Hu, H. et al. Defects identification and location of underground space for ground penetrating radar based on deep learning. Tunnel. Undergr. Sp. Technol. 140, 105278 (2023).","DOI":"10.1016\/j.tust.2023.105278"},{"key":"59135_CR46","doi-asserted-by":"crossref","unstructured":"Carion, N. et al. End-to-end object detection with transformers. In European Conference on Computer Vision. 213\u2013229. (Springer, 2020).","DOI":"10.1007\/978-3-030-58452-8_13"},{"key":"59135_CR47","doi-asserted-by":"crossref","unstructured":"Zhao, Y. et al.  Detrs beat yolos on real-time object detection. In Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition. 16965\u201316974 (2024).","DOI":"10.1109\/CVPR52733.2024.01605"},{"key":"59135_CR48","doi-asserted-by":"crossref","unstructured":"Xie, Y. et al.  Localization,balance, and affinity: A stronger multifaceted collaborative salient object detector in remote sensing images. In  IEEE Transactions on Geoscience and Remote Sensing. Vol.  63. 1\u201317. 10.1109\/TGRS.2024.3506630 (2024).","DOI":"10.1109\/TGRS.2024.3506630"},{"key":"59135_CR49","doi-asserted-by":"publisher","first-page":"103950","DOI":"10.1016\/j.jag.2024.103950","volume":"131","author":"Y Xie","year":"2024","unstructured":"Xie, Y. et al. Landslide extraction from aerial imagery considering context association characteristics. Int. J. Appl. Earth Obs. Geoinf. 131, 103950. https:\/\/doi.org\/10.1016\/j.jag.2024.103950 (2024).","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"issue":"1","key":"59135_CR50","doi-asserted-by":"publisher","first-page":"10990","DOI":"10.1038\/s41598-025-96024-4","volume":"15","author":"W Song","year":"2025","unstructured":"Song, W., Cui, X., Xie, Y., Wang, G. & Ma, J. \u0130\u00e7 mekan sahneleri i\u00e7in detayl\u0131 semantik i\u015fbirlik\u00e7i a\u011f \u00fczerinden monok\u00fcler derinlik tahmini. Sci. Rep. 15 (1), 10990. https:\/\/doi.org\/10.1038\/s41598-025-96024-4 (2025).","journal-title":"Sci. Rep."},{"key":"59135_CR51","doi-asserted-by":"publisher","first-page":"114439","DOI":"10.1016\/j.engappai.2026.114439","volume":"173","author":"W Song","year":"2026","unstructured":"Song, W. et al. A visual attention-guided approach for concrete crack detection in complex environments. Eng. Appl. Artif. Intell. 173, 114439. https:\/\/doi.org\/10.1016\/j.engappai.2026.114439 (2026).","journal-title":"Eng. Appl. Artif. Intell."},{"key":"59135_CR52","unstructured":"GEO Dataset. Roboflow Universe. https:\/\/universe.roboflow.com\/geodata-dlkoe\/geo-b8bx8. Accessed 15 Jan 2025  (Roboflow, 2024)."},{"key":"59135_CR53","unstructured":"GPR_data Dataset. Roboflow Universe. https:\/\/universe.roboflow.com\/smart-gio-lab\/gpr_data-nfekx. Accessed 15 Jan 2025 (Roboflow, 2023)."},{"key":"59135_CR54","unstructured":"aaaa Dataset. Roboflow Universe. https:\/\/universe.roboflow.com\/kiseki\/aaaa-nmpuo. Accessed 15 Jan 2025 (Roboflow, 2024)."},{"key":"59135_CR55","unstructured":"Deep Helder Fresh Dataset. Roboflow Universe. https:\/\/universe.roboflow.com\/kayur-patel-qvaho\/deep-helder-fresh Accessed 15 Jan 2025 (Roboflow, 2023)."},{"issue":"7","key":"59135_CR56","doi-asserted-by":"publisher","first-page":"677","DOI":"10.3390\/machines11070677","volume":"11","author":"M Hussain","year":"2023","unstructured":"Hussain, M. YOLO-v1 to YOLO-v8, the rise of YOLO and its complementary nature toward digital manufacturing and industrial defect detection. Machines 11 (7), 677 (2023).","journal-title":"Machines"},{"key":"59135_CR57","doi-asserted-by":"crossref","unstructured":"Li, F. Detection and Identification of Daylily Maturity Based on YOLOv8. (2024).","DOI":"10.5954\/ICAROB.2024.OS3-1"},{"issue":"13","key":"59135_CR58","doi-asserted-by":"publisher","first-page":"2331","DOI":"10.3390\/rs16132331","volume":"16","author":"S Liu","year":"2024","unstructured":"Liu, S., Cao, L. & Li, Y. Lightweight pedestrian detection network for UAV remote sensing images based on strideless pooling. Remote Sens. 16 (13), 2331 (2024).","journal-title":"Remote Sens."},{"issue":"15","key":"59135_CR59","doi-asserted-by":"publisher","first-page":"6710","DOI":"10.3390\/app14156710","volume":"14","author":"L Gao","year":"2024","unstructured":"Gao, L. et al. A lightweight YOLOv8 model for apple leaf disease detection. Appl. Sci. 14 (15), 6710 (2024).","journal-title":"Appl. Sci."},{"key":"59135_CR60","doi-asserted-by":"crossref","unstructured":"Ling, Z., Xin, Q., Lin, Y., Su, G. & Shui, Z. Optimization of autonomous driving image detection based on RFAConv and triplet attention. arXiv preprint arXiv:2407.09530 (2024).","DOI":"10.54254\/2755-2721\/77\/2024MA0067"},{"issue":"2","key":"59135_CR61","doi-asserted-by":"publisher","first-page":"60","DOI":"10.1007\/s11063-024-11464-9","volume":"56","author":"J Lei","year":"2024","unstructured":"Lei, J., Shu, C., Xu, Q., Yu, Y. & Yang, S. FCPFNet: Feature complementation network with pyramid fusion for semantic segmentation. Neural Process. Lett. 56 (2), 60 (2024).","journal-title":"Neural Process. Lett."},{"key":"59135_CR62","doi-asserted-by":"crossref","unstructured":"Daihui, L., Lei, Y., Shangyou, Z. & Chengxu, M. Design of convolutional neural network based on reticulated convolution module. In 2019 IEEE 9th International Conference on Electronics Information and Emergency Communication (ICEIEC). . 1\u20134. (IEEE, 2019).","DOI":"10.1109\/ICEIEC.2019.8784673"},{"key":"59135_CR63","unstructured":"Jung, W. et al. Restructuring batch normalization to accelerate CNN training. Proc. Mach. Learn. Syst. 1, 14\u201326 (2019)."},{"key":"59135_CR64","unstructured":"Peng, J., Xie, L., Zhang, Z., Tan, T. & Wang, J. Accelerating deep neural networks with spatial bottleneck modules. arXiv preprint arXiv:180902601 (2018)."},{"key":"59135_CR65","first-page":"21002","volume":"33","author":"X Li","year":"2020","unstructured":"Li, X. et al. Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Adv. Neural. Inf. Process. Syst. 33, 21002\u201321012 (2020).","journal-title":"Adv. Neural. Inf. Process. Syst."},{"key":"59135_CR66","doi-asserted-by":"crossref","unstructured":"Zheng, Z. et al. Distance-IoU loss: Faster and better learning for bounding box regression. In Proceedings of the AAAI Conference on Artificial Intelligence. Vol. 34(07). 12993\u201313000 (2020).","DOI":"10.1609\/aaai.v34i07.6999"},{"key":"59135_CR67","doi-asserted-by":"crossref","unstructured":"Han, K. et al. Ghostnet: More features from cheap operations. In Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition. 1580\u20131589 (2020).","DOI":"10.1109\/CVPR42600.2020.00165"},{"key":"59135_CR68","doi-asserted-by":"publisher","first-page":"2361","DOI":"10.1109\/ACCESS.2023.3234108","volume":"11","author":"J Chi","year":"2023","unstructured":"Chi, J., Guo, S., Zhang, H. & Shan, Y. L-GhostNet: Extract better quality features. IEEE Access 11, 2361\u20132374 (2023).","journal-title":"IEEE Access"},{"issue":"20","key":"59135_CR69","doi-asserted-by":"publisher","first-page":"4932","DOI":"10.3390\/rs15204932","volume":"15","author":"J Cao","year":"2023","unstructured":"Cao, J., Bao, W., Shang, H., Yuan, M. & Cheng, Q. GCL-YOLO: A GhostConv-based lightweight yolo network for UAV small object detection. Remote Sens. 15 (20), 4932 (2023).","journal-title":"Remote Sens."},{"issue":"16","key":"59135_CR70","doi-asserted-by":"publisher","first-page":"7190","DOI":"10.3390\/s23167190","volume":"23","author":"G Wang","year":"2023","unstructured":"Wang, G. et al. UAV-YOLOv8: A small-object-detection model based on improved YOLOv8 for UAV aerial photography scenarios. Sensors 23 (16), 7190 (2023).","journal-title":"Sensors"}],"container-title":["Scientific Reports"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.nature.com\/articles\/s41598-026-59135-0","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41598-026-59135-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41598-026-59135-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,10,5]],"date-time":"2026-10-05T00:08:34Z","timestamp":1791158914000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.nature.com\/articles\/s41598-026-59135-0"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,7,5]]},"references-count":70,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2026,12]]}},"alternative-id":["59135"],"URL":"https:\/\/doi.org\/10.1038\/s41598-026-59135-0","relation":{},"ISSN":["2045-2322"],"issn-type":[{"value":"2045-2322","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,7,5]]},"assertion":[{"value":"18 February 2026","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"18 June 2026","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"5 July 2026","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"The authors declare no competing interests.","order":1,"name":"Ethics","label":"Competing interests","group":{"name":"EthicsHeading","label":"Declarations"}}],"article-number":"30815"}}