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The next work is to recognize the reconstruction area of the reconstructed leading\/trailing edge\u2019s image. To accelerate this process, an anchor-free neural network model based on Transformer was proposed, named Leading\/trailing Edge Transformer (LETR). LETR extracts image features from an aspect of mixed frequency and channel domain. We also integrated LETR with the newest meta-Acon activation function. We tested our model on the self-made dataset LDEG2021 on a single GPU and got an mAP of 91.9%, which surpassed our baseline model, Deformable DETR, by 1.1%. Furthermore, we modified LETR\u2019s convolution layer and named the new model after Ghost Leading\/trailing Edge Transformer (GLETR) as a lightweight model for real-time detection. It is proved that GLETR has fewer weight parameters and converges faster than LETR with an acceptable decrease in mAP (0.1%) by test results. The proposed models provide the basis for subsequent parameter extraction work in the reconstruction area.<\/jats:p>","DOI":"10.1155\/2022\/3005684","type":"journal-article","created":{"date-parts":[[2022,5,14]],"date-time":"2022-05-14T20:20:13Z","timestamp":1652559613000},"page":"1-19","source":"Crossref","is-referenced-by-count":1,"title":["LETR: An End-to-End Detector of Reconstruction Area in Blades Adaptive Machining with Transformer"],"prefix":"10.1155","volume":"2022","author":[{"given":"Zikai","family":"Yin","sequence":"first","affiliation":[{"name":"Key Laboratory of High-Performance Manufacturing for Aero Engines, School of Mechanical Engineering, Northwestern Polytechnical University, 127 West Youyi Road, Beilin District, Xi\u2019an 710072, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7445-9269","authenticated-orcid":true,"given":"Yongshou","family":"Liang","sequence":"additional","affiliation":[{"name":"Key Laboratory of High-Performance Manufacturing for Aero Engines, School of Mechanical Engineering, Northwestern Polytechnical University, 127 West Youyi Road, Beilin District, Xi\u2019an 710072, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Junxue","family":"Ren","sequence":"additional","affiliation":[{"name":"Key Laboratory of High-Performance Manufacturing for Aero Engines, School of Mechanical Engineering, Northwestern Polytechnical University, 127 West Youyi Road, Beilin District, Xi\u2019an 710072, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jungang","family":"An","sequence":"additional","affiliation":[{"name":"Haimo Research Institution, 22 Technology of Fifth Road, High Technology District, Xi\u2019an 710000, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Famei","family":"He","sequence":"additional","affiliation":[{"name":"Beijing Institute of Technology, No. 5, South Street, Zhongguancun, Haidian District, Beijing 100000, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"311","reference":[{"key":"1","doi-asserted-by":"publisher","DOI":"10.1016\/j.cad.2012.04.002"},{"key":"2","doi-asserted-by":"publisher","DOI":"10.1007\/s00170-018-1771-x"},{"key":"3","doi-asserted-by":"publisher","DOI":"10.1007\/s12541-021-00586-y"},{"key":"4","first-page":"16","article-title":"Generative adversarial nets","volume":"27","author":"I. 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