SEGS-MobileNetV2: An Enhanced Lightweight CNN for Efficient Feature Representation in Industrial Defect Classification

No abstract is available for this paper.

• Publication year

  2025

• Venue

  International Conference on Virtual Reality and Visualization

• Publication date

  2025-12-19

• Fields of study

  Not labeled

• Identifiers
  DOI 10.1109/ICVRV67992.2025.00139
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar

• No claims are published for this paper.

• No concepts are published for this paper.

• ILCS: An Improved Lightweight Convolution Structure and Mixed Interactive Attention for Steel Surface Defect Classification

  2022cited by this paper
• An Improved Image Classification Method for Cervical Precancerous Lesions Based on ShuffleNet

  2022cited by this paper
• EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks

  2019cited by this paper
• Concurrent Spatial and Channel Squeeze & Excitation in Fully Convolutional Networks

  2018cited by this paper
• CBAM: Convolutional Block Attention Module

  2018cited by this paper
• MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications

  2017influential reference
• Attention is All you Need

  2017cited by this paper
• Learning Transferable Architectures for Scalable Image Recognition

  2017cited by this paper
• Squeeze-and-Excitation Networks

  2017influential reference
• ShuffleNet: An Extremely Efficient Convolutional Neural Network for Mobile Devices

  2017influential reference
• SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and <1MB model size

  2016influential reference
• Xception: Deep Learning with Depthwise Separable Convolutions

  2016cited by this paper
• Aggregated Residual Transformations for Deep Neural Networks

  2016cited by this paper
• Densely Connected Convolutional Networks

  2016cited by this paper
• Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning

  2016cited by this paper
• Deep Residual Learning for Image Recognition

  2015cited by this paper
• Going deeper with convolutions

  2014cited by this paper
• Very Deep Convolutional Networks for Large-Scale Image Recognition

  2014cited by this paper
• Neural Machine Translation by Jointly Learning to Align and Translate

  2014cited by this paper
• ImageNet classification with deep convolutional neural networks

  2012cited by this paper
• Gradient-based learning applied to document recognition

  1998cited by this paper

• No citing papers are available for this paper.