{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,10]],"date-time":"2026-03-10T02:48:13Z","timestamp":1773110893447,"version":"3.50.1"},"reference-count":52,"publisher":"MDPI AG","issue":"13","license":[{"start":{"date-parts":[[2024,6,21]],"date-time":"2024-06-21T00:00:00Z","timestamp":1718928000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Natural Science Foundation of China","award":["62271277"],"award-info":[{"award-number":["62271277"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>The recognition of data matrix (DM) codes plays a crucial role in industrial production. Significant progress has been made with existing methods. However, for low-quality images with protrusions and interruptions on the L-shaped solid edge (finder pattern) and the dashed edge (timing pattern) of DM codes in industrial production environments, the recognition accuracy rate of existing methods sharply declines due to a lack of consideration for these interference issues. Therefore, ensuring recognition accuracy in the presence of these interference issues is a highly challenging task. To address such interference issues, unlike most existing methods focused on locating the L-shaped solid edge for DM code recognition, we in this paper propose a novel DM code recognition method based on locating the L-shaped dashed edge by incorporating the prior information of the center of the DM code. Specifically, we first use a deep learning-based object detection method to obtain the center of the DM code. Next, to enhance the accuracy of L-shaped dashed edge localization, we design a two-level screening strategy that combines the general constraints and central constraints. The central constraints fully exploit the prior information of the center of the DM code. Finally, we employ libdmtx to decode the content from the precise position image of the DM code. The image is generated by using the L-shaped dashed edge. Experimental results on various types of DM code datasets demonstrate that the proposed method outperforms the compared methods in terms of recognition accuracy rate and time consumption, thus holding significant practical value in an industrial production environment.<\/jats:p>","DOI":"10.3390\/s24134042","type":"journal-article","created":{"date-parts":[[2024,6,21]],"date-time":"2024-06-21T08:50:08Z","timestamp":1718959808000},"page":"4042","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["A Data Matrix Code Recognition Method Based on L-Shaped Dashed Edge Localization Using Central Prior"],"prefix":"10.3390","volume":"24","author":[{"ORCID":"https:\/\/orcid.org\/0009-0004-9285-267X","authenticated-orcid":false,"given":"Yi","family":"Liu","sequence":"first","affiliation":[{"name":"College of Science and Technology, Ningbo University, Ningbo 315300, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yang","family":"Song","sequence":"additional","affiliation":[{"name":"College of Science and Technology, Ningbo University, Ningbo 315300, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Guiqiang","family":"Gu","sequence":"additional","affiliation":[{"name":"College of Science and Technology, Ningbo University, Ningbo 315300, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jianan","family":"Luo","sequence":"additional","affiliation":[{"name":"College of Science and Technology, Ningbo University, Ningbo 315300, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Taoan","family":"Wang","sequence":"additional","affiliation":[{"name":"College of Science and Technology, Ningbo University, Ningbo 315300, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Qiuping","family":"Jiang","sequence":"additional","affiliation":[{"name":"School of Information Science and Engineering, Ningbo University, Ningbo 315211, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2024,6,21]]},"reference":[{"key":"ref_1","first-page":"913","article-title":"Robot path optimization for spot welding applications in automotive industry","volume":"20","year":"2013","journal-title":"Teh. Vjesn.\/Tech. Gaz."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Karrach, L., and Pivar\u010diov\u00e1, E. (2021). Comparative study of data matrix codes localization and recognition methods. J. Imaging, 7.","DOI":"10.3390\/jimaging7090163"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"719658","DOI":"10.1155\/2015\/719658","article-title":"Indoor robot localization based on multidimensional scaling","volume":"11","author":"Cui","year":"2015","journal-title":"Int. J. Distrib. Sens. Netw."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Zhang, X., Zhu, S., Wang, Z., and Li, Y. (2018). Hybrid visual natural landmark\u2014Based localization for indoor mobile robots. Int. J. Adv. Robot. Syst., 15.","DOI":"10.1177\/1729881418810143"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"251","DOI":"10.1109\/70.563647","article-title":"Mobile robot localization using landmarks","volume":"13","author":"Betke","year":"1997","journal-title":"IEEE Trans. Robot. Autom."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Turygin, Y., Bo\u017eek, P., Nikitin, Y., Sosnovich, E., and Abramov, A. (2016). Enhancing the reliability of mobile robots control process via reverse validation. Int. J. Adv. Robot. Syst., 13.","DOI":"10.1177\/1729881416680521"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"515296","DOI":"10.1155\/2012\/515296","article-title":"Data matrix code location based on finder pattern detection and bar code border fitting","volume":"2012","author":"Huang","year":"2012","journal-title":"Math. Probl. Eng."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Dai, Y., Liu, L., Song, W., Du, C., and Zhao, X. (2017, January 15\u201317). The realization of identification method for datamatrix code. Proceedings of the 2017 International Conference on Progress in Informatics and Computing (PIC), Nanjing, China.","DOI":"10.1109\/PIC.2017.8359582"},{"key":"ref_9","first-page":"231","article-title":"Options to use data matrix codes in production engineering","volume":"26","author":"Karrach","year":"2018","journal-title":"Manag. Syst. Prod. Eng."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"173","DOI":"10.12913\/22998624\/94961","article-title":"Optimization of manipulation logistics using data matrix codes","volume":"12","author":"Karrach","year":"2018","journal-title":"Adv. Sci. Technol.-Res. J."},{"key":"ref_11","first-page":"154","article-title":"Recognition of data matrix codes in images and their applications in production processes","volume":"28","author":"Karrach","year":"2020","journal-title":"Manag. Syst. Prod. Eng."},{"key":"ref_12","unstructured":"(2023, December 09). Zxing. Available online: https:\/\/github.com\/zxing-cpp\/zxing-cpp\/tree\/v2.2.0."},{"key":"ref_13","unstructured":"(2022, March 15). Libdmtx. Available online: https:\/\/github.com\/NaturalHistoryMuseum\/pylibdmtx\/tree\/v0.1.10."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"128837","DOI":"10.1109\/ACCESS.2019.2939201","article-title":"A survey of deep learning-based object detection","volume":"7","author":"Jiao","year":"2019","journal-title":"IEEE Access"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Zhang, H., Shi, G., Liu, L., Zhao, M., and Liang, Z. (2018, January 7\u201310). Detection and identification method of medical label barcode based on deep learning. Proceedings of the 2018 Eighth International Conference on Image Processing Theory, Tools and Applications (IPTA), Xi\u2019an, China.","DOI":"10.1109\/IPTA.2018.8608144"},{"key":"ref_16","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, Columbus, OH, USA.","DOI":"10.1109\/CVPR.2014.81"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Girshick, R. (2015, January 7\u201313). Fast R-CNN. Proceedings of the IEEE International Conference on Computer Vision, Santiago, Chile.","DOI":"10.1109\/ICCV.2015.169"},{"key":"ref_18","first-page":"91","article-title":"Faster R-CNN: Towards real-time object detection with region proposal networks","volume":"28","author":"Ren","year":"2015","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Liu, W., Anguelov, D., Erhan, D., Szegedy, C., Reed, S., Fu, C.Y., and Berg, A.C. (2016, January 8\u201316). SSD: Single shot multibox detector. Proceedings of the Computer Vision\u2013ECCV 2016: 14th European Conference, Amsterdam, The Netherlands. Proceedings, Part I 14.","DOI":"10.1007\/978-3-319-46448-0_2"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Redmon, J., Divvala, S., Girshick, R., and Farhadi, A. (2016, January 27\u201330). You only look once: Unified, real-time object detection. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.91"},{"key":"ref_21","unstructured":"Bochkovskiy, A., Wang, C.Y., and Liao, H.Y.M. (2020). YOLOv4: Optimal speed and accuracy of object detection. arXiv."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Lin, T.Y., Doll\u00e1r, P., Girshick, R., He, K., Hariharan, B., and Belongie, S. (2017, January 21\u201326). 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_23","unstructured":"Cao, G., Xie, X., Yang, W., Liao, Q., Shi, G., and Wu, J. (2017, January 14\u201316). Feature-fused SSD: Fast detection for small objects. Proceedings of the Ninth International Conference on Graphic and Image Processing (ICGIP 2017), Qingdao, China."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"241","DOI":"10.1016\/j.ins.2020.02.067","article-title":"DC-SPP-YOLO: Dense connection and spatial pyramid pooling based YOLO for object detection","volume":"522","author":"Huang","year":"2020","journal-title":"Inf. Sci."},{"key":"ref_25","unstructured":"Redmon, J., and Farhadi, A. (2018). YOLOv3: An incremental improvement. arXiv."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Tan, M., Pang, R., and Le, Q.V. (2020, January 13\u201319). 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_27","doi-asserted-by":"crossref","unstructured":"Zhou, X., Zhuo, J., and Krahenbuhl, P. (2019, January 15\u201320). Bottom-up object detection by grouping extreme and center points. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.00094"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Lin, T.Y., Goyal, P., Girshick, R., He, K., and Doll\u00e1r, P. (2017, January 22\u201329). Focal loss for dense object detection. Proceedings of the IEEE International Conference on Computer Vision, Venice, Italy.","DOI":"10.1109\/ICCV.2017.324"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Law, H., and Deng, J. (2018, January 8\u201314). CornerNet: Detecting objects as paired keypoints. Proceedings of the European Conference on Computer Vision (ECCV), Munich, Germany.","DOI":"10.1007\/978-3-030-01264-9_45"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"3212","DOI":"10.1109\/TNNLS.2018.2876865","article-title":"Object detection with deep learning: A review","volume":"30","author":"Zhao","year":"2019","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Carion, N., Massa, F., Synnaeve, G., Usunier, N., Kirillov, A., and Zagoruyko, S. (2020, January 23\u201328). End-to-end object detection with transformers. Proceedings of the European Conference on Computer Vision, Glasgow, UK.","DOI":"10.1007\/978-3-030-58452-8_13"},{"key":"ref_32","unstructured":"Zhu, X., Su, W., Lu, L., Li, B., Wang, X., and Dai, J. (2020). Deformable DETR: Deformable transformers for end-to-end object detection. arXiv."},{"key":"ref_33","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, Montreal, BC, Canada.","DOI":"10.1109\/ICCV48922.2021.00986"},{"key":"ref_34","unstructured":"(2022, November 22). YOLOV5. Available online: https:\/\/github.com\/ultralytics\/yolov5\/tree\/v7.0."},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Almeida, T., Santos, V., Louren\u00e7o, B., and Fonseca, P. (2020, January 15\u201317). Detection of data matrix encoded landmarks in unstructured environments using deep learning. Proceedings of the 2020 IEEE International Conference on Autonomous Robot Systems and Competitions (ICARSC), Ponta Delgada, Portugal.","DOI":"10.1109\/ICARSC49921.2020.9096211"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"13","DOI":"10.1007\/s10846-021-01442-x","article-title":"Comparative analysis of deep neural networks for the detection and decoding of data matrix landmarks in cluttered indoor environments","volume":"103","author":"Almeida","year":"2021","journal-title":"J. Intell. Robot. Syst."},{"key":"ref_37","first-page":"722","article-title":"LSD: A fast line segment detector with a false detection control","volume":"32","author":"Jakubowicz","year":"2008","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"84","DOI":"10.1145\/3065386","article-title":"ImageNet classification with deep convolutional neural networks","volume":"60","author":"Krizhevsky","year":"2017","journal-title":"Commun. ACM"},{"key":"ref_39","unstructured":"Simonyan, K., and Zisserman, A. (2014). Very deep convolutional networks for large-scale image recognition. arXiv."},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., and Sun, J. (2016, January 27\u201330). Deep residual learning for image recognition. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Huang, G., Liu, Z., Van Der Maaten, L., and Weinberger, K.Q. (2017, January 21\u201326). Densely connected convolutional networks. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.243"},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Chou, T.H., Ho, C.S., and Kuo, Y.F. (2015, January 29\u201331). QR code detection using convolutional neural networks. Proceedings of the 2015 International Conference on Advanced Robotics and Intelligent Systems (ARIS), Taipei, Taiwan.","DOI":"10.1109\/ARIS.2015.7158354"},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Hansen, D.K., Nasrollahi, K., Rasmussen, C.B., and Moeslund, T.B. (2017, January 1\u20133). Real-time barcode detection and classification using deep learning. Proceedings of the International Joint Conference on Computational Intelligence, SCITEPRESS Digital Library, Funchal, Madeira, Portugal.","DOI":"10.5220\/0006508203210327"},{"key":"ref_44","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_45","unstructured":"Iandola, F.N., Han, S., Moskewicz, M.W., Ashraf, K., Dally, W.J., and Keutzer, K. (2016). SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and <0.5 MB model size. arXiv."},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Zhang, X., Zhou, X., Lin, M., and Sun, J. (2018, January 18\u201323). ShuffleNet: An extremely efficient convolutional neural network for mobile devices. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00716"},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Liao, L., Li, J., and Lu, C. (2022). Data extraction method for industrial data matrix codes based on local adjacent modules structure. Appl. Sci., 12.","DOI":"10.3390\/app12052291"},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"679","DOI":"10.1109\/TPAMI.1986.4767851","article-title":"A computational approach to edge detection","volume":"6","author":"Canny","year":"1986","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_49","unstructured":"Hough, P.V. (1962). Method and Means for Recognizing Complex Patterns. (3,069,654), U.S. Patent."},{"key":"ref_50","first-page":"62","article-title":"A threshold selection method from gray-level histograms","volume":"9","author":"Ostu","year":"1979","journal-title":"IEEE Trans. SMC"},{"key":"ref_51","unstructured":"(2024, May 25). Onbarcode. Available online: https:\/\/www.onbarcode.com\/scanner\/data_matrix.html."},{"key":"ref_52","unstructured":"(2024, May 25). Inlite. Available online: https:\/\/online-barcode-reader.inliteresearch.com\/."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/24\/13\/4042\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T15:02:18Z","timestamp":1760108538000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/24\/13\/4042"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,6,21]]},"references-count":52,"journal-issue":{"issue":"13","published-online":{"date-parts":[[2024,7]]}},"alternative-id":["s24134042"],"URL":"https:\/\/doi.org\/10.3390\/s24134042","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,6,21]]}}}