Matches in SemOpenAlex for { <https://semopenalex.org/work/W3007300669> ?p ?o ?g. }
Showing items 1 to 70 of
70
with 100 items per page.
- W3007300669 abstract "Surface defect detection uses advanced machine vision inspection technology to detect defects such as spots, pits, scratches and chromatic aberrations on the surface of the workpiece. The traditional machine vision detection method requires manual selection of defect features as the basis of defect identification, which is time-consuming and laborious and has low accuracy in defect detection. To overcome the aforementioned deficiencies, the convolutional neural network (CNN) is proposed as a deep learning model to extract the defect features autonomously in an elegant way. In this paper, two smaller convolution kernels form a parallel channel in two layers of the convolutional neural network architecture, and then the results of the operation are fused to extract multi-scale information, which increases the adaptability of the network to scale. Besides, the batch normalization (BN) is introduced into convolutional neural network to standardize the data distribution, offering an easy starting condition for training and improving the generalization characteristics of the network. A steel strip defect data sets are adopted to conform the effectiveness of the proposed method. The experimental results show that the proposed method accelerate the training process through reducing the training epoch number, the accuracy and detection consistency on the steel strip defect data sets achieve a superior performance to the existing methods." @default.
- W3007300669 created "2020-03-06" @default.
- W3007300669 creator A5003542013 @default.
- W3007300669 creator A5028055721 @default.
- W3007300669 creator A5081898726 @default.
- W3007300669 date "2019-08-04" @default.
- W3007300669 modified "2023-10-06" @default.
- W3007300669 title "Batch-normalized Convolutional Neural Networks for Defect Detection of the Steel Strip" @default.
- W3007300669 cites W2092072518 @default.
- W3007300669 cites W2100610790 @default.
- W3007300669 cites W2898597600 @default.
- W3007300669 doi "https://doi.org/10.1145/3378891.3378894" @default.
- W3007300669 hasPublicationYear "2019" @default.
- W3007300669 type Work @default.
- W3007300669 sameAs 3007300669 @default.
- W3007300669 citedByCount "1" @default.
- W3007300669 countsByYear W30073006692023 @default.
- W3007300669 crossrefType "proceedings-article" @default.
- W3007300669 hasAuthorship W3007300669A5003542013 @default.
- W3007300669 hasAuthorship W3007300669A5028055721 @default.
- W3007300669 hasAuthorship W3007300669A5081898726 @default.
- W3007300669 hasConcept C108583219 @default.
- W3007300669 hasConcept C111919701 @default.
- W3007300669 hasConcept C114614502 @default.
- W3007300669 hasConcept C136886441 @default.
- W3007300669 hasConcept C144024400 @default.
- W3007300669 hasConcept C153180895 @default.
- W3007300669 hasConcept C154945302 @default.
- W3007300669 hasConcept C19165224 @default.
- W3007300669 hasConcept C196956537 @default.
- W3007300669 hasConcept C2776436953 @default.
- W3007300669 hasConcept C33923547 @default.
- W3007300669 hasConcept C41008148 @default.
- W3007300669 hasConcept C45347329 @default.
- W3007300669 hasConcept C50644808 @default.
- W3007300669 hasConcept C81363708 @default.
- W3007300669 hasConcept C98045186 @default.
- W3007300669 hasConceptScore W3007300669C108583219 @default.
- W3007300669 hasConceptScore W3007300669C111919701 @default.
- W3007300669 hasConceptScore W3007300669C114614502 @default.
- W3007300669 hasConceptScore W3007300669C136886441 @default.
- W3007300669 hasConceptScore W3007300669C144024400 @default.
- W3007300669 hasConceptScore W3007300669C153180895 @default.
- W3007300669 hasConceptScore W3007300669C154945302 @default.
- W3007300669 hasConceptScore W3007300669C19165224 @default.
- W3007300669 hasConceptScore W3007300669C196956537 @default.
- W3007300669 hasConceptScore W3007300669C2776436953 @default.
- W3007300669 hasConceptScore W3007300669C33923547 @default.
- W3007300669 hasConceptScore W3007300669C41008148 @default.
- W3007300669 hasConceptScore W3007300669C45347329 @default.
- W3007300669 hasConceptScore W3007300669C50644808 @default.
- W3007300669 hasConceptScore W3007300669C81363708 @default.
- W3007300669 hasConceptScore W3007300669C98045186 @default.
- W3007300669 hasLocation W30073006691 @default.
- W3007300669 hasOpenAccess W3007300669 @default.
- W3007300669 hasPrimaryLocation W30073006691 @default.
- W3007300669 hasRelatedWork W2731899572 @default.
- W3007300669 hasRelatedWork W2999805992 @default.
- W3007300669 hasRelatedWork W3011074480 @default.
- W3007300669 hasRelatedWork W3116150086 @default.
- W3007300669 hasRelatedWork W3133861977 @default.
- W3007300669 hasRelatedWork W3192840557 @default.
- W3007300669 hasRelatedWork W4200173597 @default.
- W3007300669 hasRelatedWork W4291897433 @default.
- W3007300669 hasRelatedWork W4312417841 @default.
- W3007300669 hasRelatedWork W4321369474 @default.
- W3007300669 isParatext "false" @default.
- W3007300669 isRetracted "false" @default.
- W3007300669 magId "3007300669" @default.
- W3007300669 workType "article" @default.