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References from Adaptive large-kernel convolutional network with gaussian-modulated spatial calibration for tyre quality assessment. Local targets link to admitted publications; unresolved targets remain external evidence.
End-to-end tire defect detection model based on transfer learning techniques
10.1007/s00521-024-09664-4 · 2024 · External reference
The defect detection for X-ray images based on a new lightweight semantic segmentation network
10.3934/mbe.2022193 · 2022 · External reference
Defect inspection in tire radiographic image using concise semantic segmentation
10.1109/access.2020.3003089 · 2020 · External reference
Imagenet classification with deep convolutional neural networks
2012 · External reference
Deep residual learning for image recognition
2016 · External reference
Attention-based deep learning for tire defect detection: fusing local and global features in an industrial case study
10.1016/j.eswa.2025.126473 · 2025 · External reference
Very deep convolutional networks for large-scale image recognition
2015 · External reference
Densely connected convolutional networks
2017 · External reference
Efficientnet: rethinking model scaling for convolutional neural networks
2019 · External reference
An image is worth 16x16 words: transformers for image recognition at scale
2021 · External reference
Scaling up your kernels to 31x31: revisiting large kernel design in cnns
10.48550/arxiv.2203.06717 · 2022 · External reference
Unireplknet: a universal perception large-kernel convnet for audio, video, point cloud, time-series and image recognition
2024 · External reference
Tire appearance defect detection method via combining hog and lbp features
10.3389/fphy.2022.1099261 · 2023 · External reference
Tire defect detection using fully convolutional network
10.1109/access.2019.2908483 · 2019 · External reference
Research on tire crack detection using image deep learning method
10.1038/s41598-023-35227-z · 2023 · External reference
Aggregated residual transformations for deep neural networks
2017 · External reference
Condconv: conditionally parameterized convolutions for efficient inference
2019 · External reference
Dynamic convolution: attention over convolution kernels
2020 · External reference
Involution: inverting the inherence of convolution for visual recognition
2021 · External reference
Attention is all you need
2017 · External reference
Container: context aggregation networks
2021 · External reference
Matchformer: interleaving attention in transformers for feature matching
2022 · External reference
OverLoCK: an overview-first-look-closely-next ConvNet with context-mixing dynamic kernels
2025 · External reference
Spalstf: Diffusion-based generative model with bilstm and xca-transformer for spatial transcriptomics imputation
10.1371/journal.pcbi.1013954 · 2026 · External reference
Deepsge: predicting spatial gene expression using residual network with efficient channel attention and dynamic graph attention network
10.1186/s12864-026-12568-3 · 2026 · External reference
A convnet for the 2020s
2022 · External reference
Repvgg: making vgg-style convnets great again
2021 · External reference
Squeeze-and-excitation networks
2018 · External reference
Cbam: convolutional block attention module
10.1007/978-3-030-01234-2_1 · 2018 · External reference
What uncertainties do we need in bayesian deep learning for computer vision?
10.48550/arxiv.1703.04977 · 2017 · External reference
Tyrenet: a high-quality annotated dataset for tyre defect classification with deep learning models
10.17632/32b5vfj6tc.1 · 2024 · External reference
Unresolved reference
External reference
Mobilenetv2: inverted residuals and linear bottlenecks
2018 · External reference
Grad-cam: visual explanations from deep networks via gradient-based localization
10.1007/s11263-019-01228-7 · 2020 · External reference
Mobilenets: efficient convolutional neural networks for mobile vision applications
10.48550/arxiv.1704.04861 · 2017 · External reference