CDDNet: Camouflaged Defect Detection Network for Steel Surface
Luo, Qiwu; Li, Ben; Su, Jiaojiao; Yang, Chunhua; Gui, Weihua; Silvén, Olli; Liu, Li (2023-11-28)
IEEE
28.11.2023
Q. Luo et al., "CDDNet: Camouflaged Defect Detection Network for Steel Surface," in IEEE Transactions on Instrumentation and Measurement, vol. 73, pp. 1-13, 2024, Art no. 5000313, doi: 10.1109/TIM.2023.3336452
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© 2023 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists,or reuse of any copyrighted component of this work in other works.
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Julkaisun pysyvä osoite on
https://urn.fi/URN:NBN:fi:oulu-202403212380
https://urn.fi/URN:NBN:fi:oulu-202403212380
Tiivistelmä
Abstract
Accurate low-contrast defect detection has become a common bottleneck to further improve the performance of automated visual inspection (AVI) instruments. Inspired by visual crypsis, a novel concept of camouflaged defect has been proposed to assist surface defect detection, and then, a camouflaged defect detection network (CDDNet) was proposed. To be specific, a new inception dynamic texture enhanced module (IDTEM) was proposed to aggressively strengthen the indefinable boundaries and deceptive textures. To further explore spatial information over long distance, a lightweight recurrent decoupled fully connected attention (RDFCA) is designed with cost-effective computation. Finally, a new adaptive scale-equalizing pyramid convolution (ASEPC) was designed to achieve cross-scale feature fusion by exploiting the inter-layer feature correlation. The proposed CDDNet obtained competitive mean average precision (mAP) of 84.2%, 96.7%, and 67.1%, respectively, on three public datasets of NEU-DET, DAGM, and CAMO, when compared with state-of-the-arts.
Accurate low-contrast defect detection has become a common bottleneck to further improve the performance of automated visual inspection (AVI) instruments. Inspired by visual crypsis, a novel concept of camouflaged defect has been proposed to assist surface defect detection, and then, a camouflaged defect detection network (CDDNet) was proposed. To be specific, a new inception dynamic texture enhanced module (IDTEM) was proposed to aggressively strengthen the indefinable boundaries and deceptive textures. To further explore spatial information over long distance, a lightweight recurrent decoupled fully connected attention (RDFCA) is designed with cost-effective computation. Finally, a new adaptive scale-equalizing pyramid convolution (ASEPC) was designed to achieve cross-scale feature fusion by exploiting the inter-layer feature correlation. The proposed CDDNet obtained competitive mean average precision (mAP) of 84.2%, 96.7%, and 67.1%, respectively, on three public datasets of NEU-DET, DAGM, and CAMO, when compared with state-of-the-arts.
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