{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,3]],"date-time":"2026-02-03T22:03:41Z","timestamp":1770156221853,"version":"3.49.0"},"reference-count":40,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2026,2,1]],"date-time":"2026-02-01T00:00:00Z","timestamp":1769904000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100002367","name":"Chinese Academy of Sciences","doi-asserted-by":"crossref","award":["STS-HP-202202"],"award-info":[{"award-number":["STS-HP-202202"]}],"id":[{"id":"10.13039\/501100002367","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["JSAN"],"abstract":"<jats:p>The high-quality droplet formation in continuous inkjet printing (CIJ) is crucial for precise character deposition on product surfaces. This process, where a piezoelectric transducer perturbs a high-speed ink stream to generate micro-droplets, is highly sensitive to parameters like ink pressure and transducer amplitude. Suboptimal conditions lead to satellite droplet formation and charge transfer issues, adversely affecting print quality and necessitating reliable monitoring. Replacing inefficient manual inspection, this study develops MBSim-YOLO, a deep learning-based method for automated droplet detection. The proposed model enhances the YOLOv8 architecture by integrating MobileNetv3 to reduce computational complexity, a Bidirectional Feature Pyramid Network (BiFPN) for effective multi-scale feature fusion, and a Simple Attention Module (SimAM) to enhance feature representation robustness. A dataset was constructed using images captured by a CCD camera during the droplet ejection process. Experimental results demonstrate that MBSim-YOLO reduces the parameter count by 78.81% compared to the original YOLOv8. At an Intersection over Union (IoU) threshold of 0.5, the model achieved a precision of 98.2%, a recall of 99.1%, and a mean average precision (mAP) of 98.9%. These findings confirm that MBSim-YOLO achieves an optimal balance between high detection accuracy and lightweight performance, offering a viable and efficient solution for real-time, automated quality monitoring in industrial continuous inkjet printing applications.<\/jats:p>","DOI":"10.3390\/jsan15010016","type":"journal-article","created":{"date-parts":[[2026,2,2]],"date-time":"2026-02-02T09:48:08Z","timestamp":1770025688000},"page":"16","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Deep Learning-Based Ink Droplet State Recognition for Continuous Inkjet Printing"],"prefix":"10.3390","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2253-5546","authenticated-orcid":false,"given":"Jianbin","family":"Xiong","sequence":"first","affiliation":[{"name":"College of Automation, Guangdong Polytechnic Normal University, Guangzhou 510450, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jing","family":"Wang","sequence":"additional","affiliation":[{"name":"College of Automation, Guangdong Polytechnic Normal University, Guangzhou 510450, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Qi","family":"Wang","sequence":"additional","affiliation":[{"name":"College of Automation, Guangdong Polytechnic Normal University, Guangzhou 510450, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2337-215X","authenticated-orcid":false,"given":"Jianxiang","family":"Yang","sequence":"additional","affiliation":[{"name":"College of Automation, Guangdong Polytechnic Normal University, Guangzhou 510450, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiangjun","family":"Dong","sequence":"additional","affiliation":[{"name":"College of Automation, Guangdong Polytechnic Normal University, Guangzhou 510450, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Weikun","family":"Dai","sequence":"additional","affiliation":[{"name":"College of Automation, Guangdong Polytechnic Normal University, Guangzhou 510450, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Qianguang","family":"Zhang","sequence":"additional","affiliation":[{"name":"College of Automation, Guangdong Polytechnic Normal University, Guangzhou 510450, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2026,2,1]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"140079","DOI":"10.1109\/ACCESS.2021.3119219","article-title":"Classifications and applications of inkjet printing technology: A review","volume":"9","author":"Shah","year":"2021","journal-title":"IEEE Access"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"673","DOI":"10.1002\/adma.200901141","article-title":"Inkjet printing\u2014Process and its applications","volume":"22","author":"Singh","year":"2010","journal-title":"Adv. Mater."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"703","DOI":"10.1039\/b711984d","article-title":"Inkjet printing as a deposition and patterning tool for polymers and inorganic particles","volume":"4","author":"Tekin","year":"2008","journal-title":"Soft Matter"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"238","DOI":"10.3844\/ajassp.2019.238.243","article-title":"Investigating rapid thermoform tooling via additive manufacturing (3d printing)","volume":"16","author":"Haeberle","year":"2019","journal-title":"Am. J. Appl. Sci."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"537","DOI":"10.1007\/s00170-012-4425-4","article-title":"Direct writing of nanomaterials for flexible thin-film transistors (fTFTs)","volume":"64","author":"Desai","year":"2013","journal-title":"Int. J. Adv. Manuf. Technol."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"1076","DOI":"10.3844\/ajeassp.2018.1076.1085","article-title":"3D printing of porous scaffolds for medical applications","volume":"11","author":"Aljohani","year":"2018","journal-title":"Am. J. Eng. Appl. Sci."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"244","DOI":"10.3844\/ajassp.2019.244.272","article-title":"A Comprehensive Review of Additive Manufacturing (3D Printing): Processes, Applications and Future Potential","volume":"16","author":"Parupelli","year":"2019","journal-title":"Am. J. Appl. Sci."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"554","DOI":"10.1016\/j.ijpharm.2015.03.017","article-title":"Inkjet printing for pharmaceutics\u2014A review of research and manufacturing","volume":"494","author":"Ronan","year":"2015","journal-title":"Int. J. Pharm."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"247","DOI":"10.1016\/j.mattod.2014.04.027","article-title":"Inkjet printing for pharmaceutical applications","volume":"17","author":"Ryan","year":"2014","journal-title":"Mater. Today"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"015009","DOI":"10.1088\/2058-8585\/ac5a39","article-title":"Machine learning based data driven inkjet printed electronics: Jetting prediction for novel inks","volume":"7","author":"Brishty","year":"2022","journal-title":"Flex. Print. Electron."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"19669","DOI":"10.1038\/s41598-019-56198-0","article-title":"How to manipulate droplet jetting from needle type jet dispensers","volume":"9","author":"Phung","year":"2019","journal-title":"Sci. Rep."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"111552","DOI":"10.1016\/j.sna.2019.111552","article-title":"Piezoelectric micro-jet devices: A review","volume":"297","author":"Hengyu","year":"2019","journal-title":"Sens. Actuators A Phys."},{"key":"ref_13","first-page":"043003","article-title":"Review of digital printing technologies for electronic materials","volume":"5","author":"Kwon","year":"2020","journal-title":"Flex. Print. Electron."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"065101","DOI":"10.1063\/1.4879824","article-title":"An inkjet vision measurement technique for high-frequency jetting","volume":"85","author":"Kwon","year":"2014","journal-title":"Rev. Sci. Instruments"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Kwon, K., Zhang, D., and Go, H. (2015, January 27). Jetting frequency and evaporation effects on the measurement accuracy of inkjet droplet amount. Proceedings of the NIP & Digital Fabrication Conference, Portland, OR, USA.","DOI":"10.2352\/ISSN.2169-4451.2015.31.1.art00007_1"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"035101","DOI":"10.1063\/1.4940934","article-title":"Measurement of inkjet first-drop behavior using a high-speed camera","volume":"87","author":"Kwon","year":"2016","journal-title":"Rev. Sci. Instrumentss"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"83","DOI":"10.1016\/j.jmsy.2018.04.003","article-title":"In-situ droplet inspection and closed-loop control system using machine learning for liquid metal jet printing","volume":"47","author":"Tianjiao","year":"2018","journal-title":"J. Manuf. Syst."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"2093","DOI":"10.1007\/s10845-022-01977-2","article-title":"In-process monitoring and prediction of droplet quality in droplet-on-demand liquid metal jetting additive manufacturing using machine learning","volume":"33","author":"Gaikwad","year":"2022","journal-title":"J. Intell. Manuf."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"571","DOI":"10.1007\/s00500-019-04202-0","article-title":"Modeling of EHD inkjet printing performance using soft computing-based approaches","volume":"24","author":"Ball","year":"2020","journal-title":"Soft Comput."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"110125","DOI":"10.1016\/j.matdes.2021.110125","article-title":"Machine learning for 3D printed multi-materials tissue-mimicking anatomical models","volume":"211","author":"Goh","year":"2021","journal-title":"Mater. Des."},{"key":"ref_21","first-page":"101197","article-title":"Unsupervised learning for the droplet evolution prediction and process dynamics understanding in inkjet printing","volume":"35","author":"Huang","year":"2020","journal-title":"Addit. Manuf."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Lou, H., Duan, X., Guo, J., Liu, H., Gu, J., Bi, L., and Chen, H. (2023). DC-YOLOv8: Small-size object detection algorithm based on camera sensor. Electronics, 12.","DOI":"10.20944\/preprints202304.0124.v1"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Li, Y., Fan, Q., Huang, H., Han, Z., and Gu, Q. (2023). A modified YOLOv8 detection network for UAV aerial image recognition. Drones, 7.","DOI":"10.3390\/drones7050304"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"144","DOI":"10.1016\/j.jmsy.2018.01.003","article-title":"Deep learning for smart manufacturing: Methods and applications","volume":"48","author":"Wang","year":"2018","journal-title":"J. Manuf. Syst."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"8114","DOI":"10.1038\/s41598-019-44556-x","article-title":"Learning from droplet flows in microfluidic channels using deep neural networks","volume":"9","author":"Hadikhani","year":"2019","journal-title":"Sci. Rep."},{"key":"ref_26","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_27","unstructured":"Howard, A.G., Zhu, M., Chen, B., Kalenichenko, D., Wang, W., Weyand, T., Andreetto, M., and Adam, H. (2017). Mobilenets: Efficient convolutional neural networks for mobile vision applications. arXiv."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Sandler, M., Howard, A., Zhu, M., Zhmoginov, A., and Chen, L. (2018, January 18). Mobilenetv2: Inverted residuals and linear bottlenecks. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00474"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Howard, A., Sandler, M., Chu, G., Chen, L., Chen, B., Tan, M., Wang, W., Zhu, Y., Pang, R., and Vasudevan, V. (2019, January 27). Searching for mobilenetv3. Proceedings of the IEEE\/CVF International Conference on Computer Vision, Seoul, Republic of Korea.","DOI":"10.1109\/ICCV.2019.00140"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Lin, T., Doll\u00e1r, P., Girshick, R., He, K., Hariharan, B., and Belongie, S. (2017, January 21). Feature pyramid networks for object detection. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.106"},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Liu, S., Qi, L., Qin, H., Shi, J., and Jia, J. (2018, January 18). Path aggregation network for instance segmentation. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00913"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"107955","DOI":"10.1016\/j.compag.2023.107955","article-title":"Lightweight detection networks for tea bud on complex agricultural environment via improved YOLO v4","volume":"211","author":"Li","year":"2023","journal-title":"Comput. Electron. Agric."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Tan, M., Pang, R., and Le, Q.V. (2020, January 13). Efficientdet: Scalable and efficient object detection. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Seattle, WA, USA.","DOI":"10.1109\/CVPR42600.2020.01079"},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Chen, J., Mai, H., Luo, L., Chen, X., and Wu, K. (2021, January 19). Effective feature fusion network in BIFPN for small object detection. Proceedings of the 2021 IEEE International Conference on Image Processing (ICIP), Anchorage, AL, USA.","DOI":"10.1109\/ICIP42928.2021.9506347"},{"key":"ref_35","unstructured":"Yang, L., Zhang, R., Li, L., and Xie, X. (2021, January 18). Simam: A simple, parameter-free attention module for convolutional neural networks. Proceedings of the International Conference on Machine Learning, Graz, Austria."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"105062","DOI":"10.1016\/j.autcon.2023.105062","article-title":"Real-time instance-level detection of asphalt pavement distress combining space-to-depth (SPD) YOLO and omni-scale network (OSNet)","volume":"155","author":"Jiale","year":"2023","journal-title":"Autom. Constr."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"65","DOI":"10.1016\/j.cirpj.2023.07.010","article-title":"Accelerated deep-learning-based process monitoring of microfluidic inkjet printing","volume":"46","author":"Seong","year":"2023","journal-title":"CIRP J. Manuf. Sci. Technol."},{"key":"ref_38","unstructured":"Mehta, S., and Rastegari, M. (2021). Mobilevit: Light-weight, general-purpose, and mobile-friendly vision transformer. arXiv."},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Ma, N., Zhang, X., Zheng, H., and Sun, J. (2018, January 8). Shufflenet v2: Practical guidelines for efficient cnn architecture design. Proceedings of the European Conference on Computer Vision (ECCV), Munich, Germany.","DOI":"10.1007\/978-3-030-01264-9_8"},{"key":"ref_40","first-page":"9969","article-title":"GhostNetv2: Enhance cheap operation with long-range attention","volume":"35","author":"Tang","year":"2022","journal-title":"Adv. Neural Inf. Process. Syst."}],"container-title":["Journal of Sensor and Actuator Networks"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2224-2708\/15\/1\/16\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,2,3]],"date-time":"2026-02-03T05:24:30Z","timestamp":1770096270000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2224-2708\/15\/1\/16"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,2,1]]},"references-count":40,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2026,2]]}},"alternative-id":["jsan15010016"],"URL":"https:\/\/doi.org\/10.3390\/jsan15010016","relation":{},"ISSN":["2224-2708"],"issn-type":[{"value":"2224-2708","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,2,1]]}}}