{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,24]],"date-time":"2026-02-24T18:03:25Z","timestamp":1771956205069,"version":"3.50.1"},"reference-count":41,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2026,2,24]],"date-time":"2026-02-24T00:00:00Z","timestamp":1771891200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Key Research and Development Program of Henan Province of China","award":["251111221700"],"award-info":[{"award-number":["251111221700"]}]},{"name":"Key Scientific and Technological Project of Henan Province of China","award":["242102220094"],"award-info":[{"award-number":["242102220094"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["J. Imaging"],"abstract":"<jats:p>Microblind hole processing quality inspection, especially accurately identifying microblind hole contour features and precisely detecting 3D and morphological parameters, has always been challenging, especially for accurately identifying those of different sizes, depths, and contour features simultaneously. This poses a great challenge for identifying and localizing microblind hole contours based on machine vision and accurately calculating three-dimensional parameters. This study takes cigarette microblind holes (diameter of 0.1\u20130.2 mm, depth of approximately 35 \u00b5m) as the research object. It focuses on solving two major challenges: recognizing and localizing microblind hole contours in complex texture backgrounds and accurately calculating their 3D geometric morphology. An improved YOLOv11s model is proposed for microblind hole image multiobject detection with complex texture backgrounds to extract their features completely. An Area\u2013Volume Computation (AVC) algorithm, which utilizes discrete integral estimation and curve-fitting principles, is also proposed for computing their surface area and volume. The experimental results show that the precision, recall, mAP@0.5, mAP@0.5:0.95, and prediction time of the improved YOLOv11 network are 0.915, 0.948, 0.925, 0.615, and 1.27 ms, respectively. The relative errors (REs) of the surface area and volume calculation of the microblind holes are 5.236% and 3.964%, respectively. The proposed method achieves microblind hole recognition, localization and 3D morphology calculation accuracy, meeting cigarette on-site inspection criteria. Additionally, a reference for detecting other similar objects in complex texture backgrounds and accurately calculating 3D tasks is provided.<\/jats:p>","DOI":"10.3390\/jimaging12030096","type":"journal-article","created":{"date-parts":[[2026,2,24]],"date-time":"2026-02-24T16:58:18Z","timestamp":1771952298000},"page":"96","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Recognition, Localization and 3D Geometric Morphology Calculation of Microblind Holes in Complex Backgrounds Based on the Improved YOLOv11 Network and AVC Algorithm"],"prefix":"10.3390","volume":"12","author":[{"given":"Chengfen","family":"Zhang","sequence":"first","affiliation":[{"name":"College of Electrical Engineering, Henan University of Technology, Zhengzhou 450001, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Dong","family":"Xia","sequence":"additional","affiliation":[{"name":"College of Electrical Engineering, Henan University of Technology, Zhengzhou 450001, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ruizhao","family":"Chen","sequence":"additional","affiliation":[{"name":"College of Electrical Engineering, Henan University of Technology, Zhengzhou 450001, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0006-9132-1011","authenticated-orcid":false,"given":"Qunfeng","family":"Niu","sequence":"additional","affiliation":[{"name":"College of Electrical Engineering, Henan University of Technology, Zhengzhou 450001, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Tao","family":"Wang","sequence":"additional","affiliation":[{"name":"Beijing Boshenkang Technology Co., Ltd., Beijing 100102, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0007-7925-4773","authenticated-orcid":false,"given":"Li","family":"Wang","sequence":"additional","affiliation":[{"name":"College of Electrical Engineering, Henan University of Technology, Zhengzhou 450001, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2026,2,24]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1109\/TIM.2022.3214285","article-title":"A Low-Cost Low-Field Nuclear Magnetic Resonance Cryoporometry System for Nanopore Size Measurement","volume":"71","author":"Lu","year":"2022","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"108408","DOI":"10.1016\/j.measurement.2020.108408","article-title":"Measuring Measurement\u2014What Is Metrology and Why Does It Matter?","volume":"168","author":"Brown","year":"2021","journal-title":"Measurement"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"015018","DOI":"10.1088\/1361-6501\/ac39d0","article-title":"A Novel Cooling Hole Inspection Method for Turbine Blade Using 3D Reconstruction of Stereo Vision","volume":"33","author":"Cheng","year":"2022","journal-title":"Meas. Sci. Technol."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"13","DOI":"10.1016\/j.sna.2012.10.030","article-title":"Fiber Probe for Micro-Hole Measurement Based on Detection of Returning Light Energy","volume":"190","author":"Cui","year":"2013","journal-title":"Sens. Actuators A Phys."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"075902","DOI":"10.1088\/1361-6501\/ab7efc","article-title":"Development of Measurement System for Microstructures Using an Optical Fiber Probe: Improvement of Measurable Region and Depth","volume":"31","author":"Murakami","year":"2020","journal-title":"Meas. Sci. Technol."},{"key":"ref_6","first-page":"1","article-title":"Novel Compensation Method of Volumetric Errors for Micro-Coordinate Measuring Machines Using Abbe and Bryan Principles","volume":"71","author":"He","year":"2022","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"112562","DOI":"10.1016\/j.measurement.2023.112562","article-title":"Geometric Parameters Measurement for the Cooling Holes of Turbine Blade Based on Microscopic Image Sequence Topographical Reconstruction","volume":"210","author":"Li","year":"2023","journal-title":"Measurement"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"012059","DOI":"10.1088\/1757-899X\/1193\/1\/012059","article-title":"Uncertainty Determination of a Microvolume Characterized Through Confocal Microscope Technique","volume":"1193","author":"Berzal","year":"2021","journal-title":"IOP Conf. Ser. Mater. Sci. Eng."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Yuan, J., Feng, S., and Han, H. (2026). ClearSight-RS: A YOLOv5-Based Network with Dynamic Enhancement for Remote Sensing Small Target Detection. Sensors, 26.","DOI":"10.3390\/s26010117"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"115109","DOI":"10.1063\/5.0170116","article-title":"Measurement of Micropore by Resonant Probe with Microsphere","volume":"13","author":"Fang","year":"2023","journal-title":"AIP Adv."},{"key":"ref_11","first-page":"192","article-title":"Research on Micro-Hole Size Measurement Technology for Medicinal Glass Bottles Based on Vacuum Testing Techniques","volume":"52","author":"Yang","year":"2023","journal-title":"Mech. Electr. Eng. Technol."},{"key":"ref_12","first-page":"106","article-title":"A Machine Vision-Based Method for Detecting Micro-Holes in Inkjet Print Heads","volume":"48","author":"Shen","year":"2020","journal-title":"Mach. Tool Hydraul."},{"key":"ref_13","first-page":"53","article-title":"Micro-Hole Detection of Glass Ampoules Based on Improved GoogLeNet","volume":"59","author":"Cao","year":"2022","journal-title":"J. Sichuan Univ. (Nat. Sci. Ed.)"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"127210","DOI":"10.1016\/j.neucom.2023.127210","article-title":"MPQ-YOLO: Ultra Low Mixed-Precision Quantization of YOLO for Edge Devices Deployment","volume":"574","author":"Liu","year":"2023","journal-title":"Neurocomputing"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"109147","DOI":"10.1016\/j.isci.2024.109147","article-title":"Design of Intelligent Inspection System for Solder Paste Printing Defects Based on Improved YOLOX","volume":"27","author":"Kong","year":"2024","journal-title":"iScience"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Jia, K., Niu, Q., Wang, L., Niu, Y., and Ma, W. (2023). A New Efficient Multi-Object Detection and Size Calculation for Blended Tobacco Shreds Using an Improved YOLOv7 Network and LWC Algorithm. Sensors, 23.","DOI":"10.3390\/s23208380"},{"key":"ref_17","first-page":"3543","article-title":"Lightweight Attention-Guided YOLO with Level Set Layer for Landslide Detection from Optical Satellite Images","volume":"17","author":"Yang","year":"2024","journal-title":"IEEE J. Sel. Top. Appl. Earth Observ. Remote Sens. Sens."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"109090","DOI":"10.1016\/j.compag.2024.109090","article-title":"Agricultural Object Detection with You Only Look Once (YOLO) Algorithm: A Bibliometric and Systematic Literature Review","volume":"223","author":"Badgujar","year":"2024","journal-title":"Comput. Electron. Agric."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"113936","DOI":"10.1016\/j.measurement.2023.113936","article-title":"STF-YOLO: A Small Target Detection Algorithm for UAV Remote Sensing Images Based on Improved SwinTransformer and Class Weighted Classification Decoupling Head","volume":"224","author":"Hui","year":"2024","journal-title":"Measurement"},{"key":"ref_20","first-page":"1","article-title":"TD-YOLOA: An Efficient YOLO Network With Attention Mechanism for Tire Defect Detection","volume":"72","author":"Peng","year":"2023","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"381","DOI":"10.32604\/jai.2025.071674","article-title":"DSC-RTDETR: An Improved RTDETR Based Crack Detection on Concrete Surface","volume":"7","author":"Zhou","year":"2025","journal-title":"J. Artif. Intell."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"111655","DOI":"10.1016\/j.measurement.2022.111655","article-title":"Fast Vehicle Detection Algorithm in Traffic Scene Based on Improved SSD","volume":"201","author":"Chen","year":"2022","journal-title":"Measurement"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Liu, B., Yang, L., and Zhu, L. (2025). Research on Object Detection in Autonomous Driving Road Scene Based on Improved YOLOv11 Algorithm. Proceedings of the 2025 International Conference on Machine Learning and Neural Networks (ILMN \u201925), Association for Computing Machinery.","DOI":"10.1145\/3747227.3747249"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Jiang, C., Ma, H., and Li, L. (2022). IRNet: An Improved RetinaNet Model for Face Detection. Proceedings of the 7th International Conference on Image, Vision and Computing (ICIVC), Xi\u2019an, China, IEEE.","DOI":"10.1109\/ICIVC55077.2022.9886975"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Li, Q., Ge, Z., Wang, F., Luo, X., Yang, Y., and Yu, T. (2022). Small Target Repair Parts Detection Algorithm Based on Improved YOLOv5. Proceedings of the 10th International Conference on Information Systems and Computing Technology (ISCTech), IEEE.","DOI":"10.1109\/ISCTech58360.2022.00073"},{"key":"ref_26","first-page":"251","article-title":"A Small Object Detection Algorithm Based on Improved YOLOv5","volume":"51","author":"Guo","year":"2022","journal-title":"J. Univ. Electron. Sci. Technol. China"},{"key":"ref_27","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_28","doi-asserted-by":"crossref","first-page":"10","DOI":"10.1364\/JOSAA.403850","article-title":"Improved SSD Network for Accurate Detection of Optic Disc and Fovea and Application in Excyclotropia Screening","volume":"38","author":"Xie","year":"2021","journal-title":"J. Opt. Soc. Am. A"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Liu, W., Anguelov, D., Erhan, D., Szegedy, C., Reed, S., Fu, C.Y., and Berg, A.C. (2016). SSD: Single Shot MultiBox Detector. Proceedings of the 14th European Conference on Computer Vision (ECCV), Amsterdam, The Netherlands, Springer.","DOI":"10.1007\/978-3-319-46448-0_2"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"1066","DOI":"10.1016\/j.procs.2022.01.135","article-title":"A Review of YOLO Algorithm Developments","volume":"199","author":"Jiang","year":"2022","journal-title":"Procedia Comput. Sci."},{"key":"ref_31","unstructured":"Bochkovskiy, A., Wang, C.Y., and Liao, H.Y.M. (2020). YOLOv4: Optimal Speed and Accuracy of Object Detection. arXiv."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"41391","DOI":"10.1364\/OE.502163","article-title":"Footsteps Detection and Identification Based on Distributed Optical Fiber Sensor and Double-YOLO Model","volume":"31","author":"Shi","year":"2023","journal-title":"Opt. Express"},{"key":"ref_33","unstructured":"Simonyan, K., and Zisserman, A. (2015, January 7\u20139). Very Deep Convolutional Networks for Large-Scale Image Recognition. Proceedings of the International Conference on Learning Representations (ICLR), San Diego, CA, USA."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Tan, M., Pang, R., and Le, Q.V. (2020). EfficientDet: Scalable and Efficient Object Detection. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Seattle, WA, USA, IEEE.","DOI":"10.1109\/CVPR42600.2020.01079"},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Han, K., Wang, Y., Tian, Q., Guo, J., Xu, C., and Xu, C. (2020). GhostNet: More Features From Cheap Operations. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Seattle, WA, USA, IEEE.","DOI":"10.1109\/CVPR42600.2020.00165"},{"key":"ref_36","unstructured":"Redmon, J., and Farhadi, A. (2018). YOLOv3: An Incremental Improvement. arXiv."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"318","DOI":"10.1109\/TPAMI.2018.2858826","article-title":"Focal Loss for Dense Object Detection","volume":"42","author":"Lin","year":"2020","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_38","unstructured":"Wang, A., Chen, H., Liu, L., Chen, K., Lin, Z., Han, J., and Ding, G. (2024). YOLOv10: Real-Time End-to-End Object Detection. arXiv."},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Feng, C., Zhong, Y., Gao, Y., Scott, M.R., and Huang, W. (2021). TOOD: Task-aligned One-stage Object Detection. arXiv.","DOI":"10.1109\/ICCV48922.2021.00349"},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Zhao, Y., Lv, W., Xu, S., Wei, J., Wang, G., Dang, Q., Liu, Y., and Chen, J. (2024). DETRs Beat YOLOs on Real-time Object Detection. arXiv.","DOI":"10.1109\/CVPR52733.2024.01605"},{"key":"ref_41","unstructured":"Kazhdan, M., Bolitho, M., and Hoppe, H. (2006). Poisson Surface Reconstruction. Proceedings of the Fourth Eurographics Symposium on Geometry Processing, Eurographics Association."}],"container-title":["Journal of Imaging"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2313-433X\/12\/3\/96\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,2,24]],"date-time":"2026-02-24T17:08:54Z","timestamp":1771952934000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2313-433X\/12\/3\/96"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,2,24]]},"references-count":41,"journal-issue":{"issue":"3","published-online":{"date-parts":[[2026,3]]}},"alternative-id":["jimaging12030096"],"URL":"https:\/\/doi.org\/10.3390\/jimaging12030096","relation":{},"ISSN":["2313-433X"],"issn-type":[{"value":"2313-433X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,2,24]]}}}