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The framework is designed based on recent popular convolutional neural networks and feature pyramid. To further boost the representation power of the network, a new feature weighting module (FWM) was proposed to recalibrate the channel-wise attention and increase the weights of valid features. The model was trained and tested on a self-built dataset, which consisted of 1916 images and contained three defect types: ablation, crack and coating missing. Extensive experimental results verify the effectiveness of the proposed FWM and show that the proposed method can accurately detect engine defects of different scales and different locations. Our method obtains 89.4% mAP and can run at 6FPS, which surpasses other state-of-the-art detection methods and can quickly provide diagnostic basis for aircraft maintenance inspectors in practical applications. <\/jats:p>","DOI":"10.1142\/s0219691320500125","type":"journal-article","created":{"date-parts":[[2019,11,29]],"date-time":"2019-11-29T03:49:11Z","timestamp":1574999351000},"page":"2050012","source":"Crossref","is-referenced-by-count":17,"title":["Feature weighting network for aircraft engine defect detection"],"prefix":"10.1142","volume":"18","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5058-0523","authenticated-orcid":false,"given":"Liqiong","family":"Chen","sequence":"first","affiliation":[{"name":"School of Electronic Information, Wuhan University, Wuhan 430072, P. R. 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