{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,28]],"date-time":"2026-04-28T19:09:19Z","timestamp":1777403359718,"version":"3.51.4"},"reference-count":35,"publisher":"MDPI AG","issue":"6","license":[{"start":{"date-parts":[[2024,3,20]],"date-time":"2024-03-20T00:00:00Z","timestamp":1710892800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Foundation of China","doi-asserted-by":"publisher","award":["52175130"],"award-info":[{"award-number":["52175130"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>High-frequency workpieces have the characteristics of complex intra-class textures and small differences between classes, leading to the problem of low recognition rates when existing models are applied to the recognition of high-frequency workpiece images. We propose in this paper a novel high-frequency workpiece image recognition model that uses EfficientNet-B1 as the basic network and integrates multi-level network structures, designated as ML-EfficientNet-B1. Specifically, a lightweight mixed attention module is first introduced to extract global workpiece image features with strong illumination robustness, and the global recognition results are obtained through the backbone network. Then, the weakly supervised area detection module is used to locate the locally important areas of the workpiece and is introduced into the branch network to obtain local recognition results. Finally, the global and local recognition results are combined in the branch fusion module to achieve the final recognition of high-frequency workpiece images. Experimental results show that compared with various image recognition models, the proposed ML-EfficientNet-B1 model has stronger adaptability to illumination changes, significantly improves the performance of high-frequency workpiece recognition, and the recognition accuracy reaches 98.3%.<\/jats:p>","DOI":"10.3390\/s24061982","type":"journal-article","created":{"date-parts":[[2024,3,20]],"date-time":"2024-03-20T10:44:33Z","timestamp":1710931473000},"page":"1982","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["High-Frequency Workpiece Image Recognition Model Integrating Multi-Level Network Structure"],"prefix":"10.3390","volume":"24","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9309-2121","authenticated-orcid":false,"given":"Yang","family":"Ou","sequence":"first","affiliation":[{"name":"School of Mechanical Engineering, Chengdu University, Chengdu 610106, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chenglong","family":"Sun","sequence":"additional","affiliation":[{"name":"School of Mechanical Engineering, Southwest Jiaotong University, Chengdu 610031, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Rong","family":"Yuan","sequence":"additional","affiliation":[{"name":"School of Mechanical Engineering, Chengdu University, Chengdu 610106, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jianqiao","family":"Luo","sequence":"additional","affiliation":[{"name":"College of Electrical and Information Engineering, Hunan University, Changsha 410082, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2024,3,20]]},"reference":[{"key":"ref_1","first-page":"4","article-title":"Made in China 2025","volume":"2","author":"Meissner","year":"2016","journal-title":"Mercat. Inst. China Studies. Pap. China"},{"key":"ref_2","first-page":"78","article-title":"Evolving made in China 2025","volume":"8","author":"Zenglein","year":"2018","journal-title":"MERICS Pap. China"},{"key":"ref_3","first-page":"2273","article-title":"Intelligent Manufacturing\u2014Main Direction of \u201cMade in China 2025\u201d","volume":"26","author":"Zhou","year":"2015","journal-title":"China Mech. Eng."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Zhao, H., Jia, J., and Koltun, V. (2020, January 13\u201319). Exploring self-attention for image recognition. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Seattle, WA, USA.","DOI":"10.1109\/CVPR42600.2020.01009"},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Sampurno, R.M., Liu, Z., Abeyrathna, R.M., and Ahamed, T. (2024). Intrarow Uncut Weed Detection Using You-Only-Look-Once Instance Segmentation for Orchard Plantations. Sensors, 24.","DOI":"10.3390\/s24030893"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Alam, L., and Kehtarnavaz, N. (2024). Improving Recognition of Defective Epoxy Images in Integrated Circuit Manufacturing by Data Augmentation. Sensors, 24.","DOI":"10.3390\/s24030738"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"6308","DOI":"10.1109\/JSTARS.2020.3026724","article-title":"Support vector machine versus random forest for remote sensing image classification: A meta-analysis and systematic review","volume":"13","author":"Sheykhmousa","year":"2020","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Zhao, C., Qin, Y., and Zhang, B. (2023). Adversarially Learning Occlusions by Backpropagation for Face Recognition. Sensors, 23.","DOI":"10.3390\/s23208559"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"61855","DOI":"10.1109\/ACCESS.2023.3287854","article-title":"Image Generation and Recognition Technology Based on Attention Residual GAN","volume":"11","author":"Wang","year":"2023","journal-title":"IEEE Access"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Yi, X., Qian, C., Wu, P., Maponde, B.T., Jiang, T., and Ge, W. (2023). Research on Fine-Grained Image Recognition of Birds Based on Improved YOLOv5. Sensors, 23.","DOI":"10.3390\/s23198204"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Li, Z., Tang, H., Peng, Z., Qi, G., and Tang, J. (2023). Knowledge-guided semantic transfer network for few-shot image recognition. IEEE Trans. Neural Netw. Learn. Syst.","DOI":"10.1109\/TNNLS.2023.3240195"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"1997","DOI":"10.1007\/s10489-022-03486-4","article-title":"Teacher-student collaborative knowledge distillation for image classification","volume":"53","author":"Xu","year":"2023","journal-title":"Appl. Intell."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"641","DOI":"10.1038\/s41586-020-1942-4","article-title":"Fully hardware-implemented memristor convolutional neural network","volume":"577","author":"Yao","year":"2020","journal-title":"Nature"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"8489","DOI":"10.1038\/s41467-023-43944-2","article-title":"Full hardware implementation of neuromorphic visual system based on multimodal optoelectronic resistive memory arrays for versatile image processing","volume":"14","author":"Zhou","year":"2023","journal-title":"Nat. Commun."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"9307","DOI":"10.1007\/s00521-019-04337-z","article-title":"A classification model of railway fasteners based on computer vision","volume":"31","author":"Ou","year":"2019","journal-title":"Neural Comput. Appl."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"16181","DOI":"10.1007\/s00521-021-06218-w","article-title":"Topic-based label distribution learning to exploit label ambiguity for scene classification","volume":"33","author":"Luo","year":"2021","journal-title":"Neural Comput. Appl."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Luo, J., Wang, Y., Ou, Y., He, B., and Li, B. (2021). Neighbour-based label distribution learning to model label ambiguity for aerial scene classification. Remote Sens., 13.","DOI":"10.3390\/rs13040755"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.inffus.2023.02.012","article-title":"CFCNN: A novel convolutional fusion framework for collaborative fault identification of rotating machinery","volume":"95","author":"Xu","year":"2023","journal-title":"Inf. Fusion"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Xu, Y., Yan, X., Sun, B., Feng, K., Kou, L., Chen, Y., Li, Y., Chen, H., Tian, E., and Ni, Q. (2023). Online Knowledge Distillation Based Multiscale Threshold Denoising Networks for Fault Diagnosis of Transmission Systems. IEEE Trans. Transp. Electrif.","DOI":"10.1109\/TTE.2023.3313986"},{"key":"ref_20","first-page":"128","article-title":"Recognition algorithm for metal parts based on ring template matching","volume":"40","author":"Xu","year":"2021","journal-title":"Transducer Microsyst. Technol."},{"key":"ref_21","first-page":"196","article-title":"Fast identification algorithm of high frequency components based on ring segmentation","volume":"12","author":"Yin","year":"2022","journal-title":"Mach. Des. Manuf."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Wang, Y., Chen, H., Zhao, K., and Zhao, P. (2018, January 5\u20138). A mechanical part sorting method based on fast template matching. Proceedings of the 2018 IEEE International Conference on Mechatronics, Robotics and Automation (ICMRA), Changchun, China.","DOI":"10.1109\/ICMRA.2018.8490571"},{"key":"ref_23","first-page":"30","article-title":"High frequency workpiece deep learning recognition algorithm based on joint loss supervision","volume":"52","author":"Yang","year":"2023","journal-title":"Mach. Build. Autom."},{"key":"ref_24","first-page":"572","article-title":"Classification algorithm of main bearing cap based on deep learning","volume":"42","author":"Zhang","year":"2021","journal-title":"J. Graph."},{"key":"ref_25","unstructured":"Szegedy, C., Vanhoucke, V., Loffe, S., Shlens, J., and Wojna, Z. (July, January 26). Rethinking the inception architecture for computer vision. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Chollet, F. (2017, January 21\u201326). Xception: Deep learning with depthwise separable convolutions. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.195"},{"key":"ref_27","unstructured":"Tan, M., and Le, Q. (2019, January 9\u201315). Efficientnet: Rethinking model scaling for convolutional neural networks. Proceedings of the International Conference on Machine Learning (ICML), Long Beach, CA, USA."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Woo, S., Park, J., Lee, J.Y., and Kweon, I. (2018, January 8\u201314). Cbam: Convolutional block attention module. Proceedings of the European Conference on Computer Vision (ECCV), Munich, Germany.","DOI":"10.1007\/978-3-030-01234-2_1"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Wang, Q., Wu, B., Zhu, P., Li, P., Zuo, W., and Hu, Q. (2020, January 13\u201319). ECA-Net: Efficient channel attention for deep convolutional neural networks. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Seattle, WA, USA.","DOI":"10.1109\/CVPR42600.2020.01155"},{"key":"ref_30","unstructured":"Kingma, D.P., and Ba, J.L. (2014). Adam: A method for stochastic optimization. arXiv."},{"key":"ref_31","unstructured":"Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., and Antiga, L. (2019). Pytorch: An imperative style, high-performance deep learning library. Adv. Neural Inf. Process. Syst., 32."},{"key":"ref_32","first-page":"337","article-title":"Recognition Algorithm Based on Convolution Neural Network for the Mechanical Parts","volume":"484","author":"Duan","year":"2019","journal-title":"Adv. Manuf. Autom. VIII"},{"key":"ref_33","first-page":"82","article-title":"Part recognition based on improved convolution neural network","volume":"5","author":"Yang","year":"2022","journal-title":"Instrum. Tech. Sens."},{"key":"ref_34","unstructured":"Li, C., Zhou, A., and Yao, A. (2024). NOAH: Learning Pairwise Object Category Attentions for Image Classification. arXiv."},{"key":"ref_35","unstructured":"Lin, H., Miao, L., and Ziai, A. (2023). RAFIC: Retrieval-Augmented Few-shot Image Classification. arXiv."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/24\/6\/1982\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T14:16:45Z","timestamp":1760105805000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/24\/6\/1982"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,3,20]]},"references-count":35,"journal-issue":{"issue":"6","published-online":{"date-parts":[[2024,3]]}},"alternative-id":["s24061982"],"URL":"https:\/\/doi.org\/10.3390\/s24061982","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,3,20]]}}}