TEEP-RCNN: Texture-Enhanced Edge-aware Perception for Steel Surface Defect Detection via Improved Convolutional Block Attention in Faster R-CNN

arXiv:2609.28077 · cs.CV · Submitted 2026-09-23 · Read on arXiv

cs.CV

Submitted: 2026-09-23

Updated: 2026-09-23

Code: https://github.com/ultralytics/ultralytics

Terminology

• Transformer — 11× in this paper · explained in 2 episodes
• Benchmark — 8× in this paper · explained in 2 episodes
• Accuracy — 7× in this paper · explained in 1 episode
• Regularization — 7× in this paper · explained in 3 episodes
• Ablation — 4× in this paper · explained in 1 episode
• Adaptation — 3× in this paper · explained in 1 episode
• Conference on Computer Vision and Pattern Recognition — 3× in this paper
• Feature Pyramid Networks — 3× in this paper
• International Conference on Learning Representations — 3× in this paper
• Region Proposal Network — 3× in this paper
• Attention Mechanism — 2× in this paper · explained in 3 episodes
• Calibration — 2× in this paper · explained in 7 episodes
• Consistency — 2× in this paper · explained in 3 episodes
• Convolutional Block Attention Module — 2× in this paper
• Data Augmentation — 2× in this paper · explained in 7 episodes
• Distribution Shift — 2× in this paper · explained in 6 episodes
• European Conference on Computer Vision — 2× in this paper
• IEEE International Conference on Computer Vision — 2× in this paper
• Sparse Attention — 2× in this paper · explained in 5 episodes
• Ablation study — 1× in this paper · explained in 1 episode
• Alignment — 1× in this paper · explained in 1 episode
• Covariate Shift — 1× in this paper · explained in 2 episodes
• Decoupling — 1× in this paper · explained in 2 episodes
• Edge Deployment — 1× in this paper · explained in 2 episodes
• Edge Devices — 1× in this paper · explained in 2 episodes
• Fine-Tuning — 1× in this paper · explained in 5 episodes
• Generalizability — 1× in this paper · explained in 6 episodes
• Gradient Flow — 1× in this paper · explained in 2 episodes
• Robustness — 1× in this paper · explained in 5 episodes
• Trace — 1× in this paper · explained in 2 episodes
• Transfer Learning — 1× in this paper · explained in 10 episodes

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