MFAM-Net:A Surface Defect Detection Network for Strip Steel via Multiscale Feature Fusion and Attention Mechanism
Author:
Affiliation:
1. College of Electronic Information Engineering, Shandong University of Science and Technology,Qingdao,Shandong,China,266590
2. SuperRay Technology Co., Ltd,Qingdao,Shandong,China,266199
Funder
SuperRay Technology Co., Ltd. This work is supported by National Key R&D Program of China
NSFC
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx7/10403657/10403658/10403686.pdf?arnumber=10403686
Reference16 articles.
1. MSFT-YOLO: Improved YOLOv5 Based on Transformer for Detecting Defects of Steel Surface
2. DCAM-Net: A Rapid Detection Network for Strip Steel Surface Defects Based on Deformable Convolution and Attention Mechanism
3. Infrared Thermal Imaging-Based Crack Detection Using Deep Learning
4. Region‐based fully convolutional networks with deformable convolution and attention fusion for steel surface defect detection in industrial Internet of Things
5. Res2Net: A New Multi-Scale Backbone Architecture
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1. EFS-YOLO: a lightweight network based on steel strip surface defect detection;Measurement Science and Technology;2024-08-06
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