An Enhanced YOLOv4 Model With Self-Dependent Attentive Fusion and Component Randomized Mosaic Augmentation for Metal Surface Defect Detection

An Enhanced YOLOv4 Model With Self-Dependent Attentive Fusion and Component Randomized Mosaic Augmentation for Metal Surface Defect Detection

Metal surface quality control is significant in the production line of metal products. Detecting metal surface defects is challenging due to the various types and morphological patterns. Recent advances have witnessed deep learning-based automated optical inspection systems as a promising solution....

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Bibliographic Details
Main Authors: Chenglong Wang, Ziran Zhou, Zhiming Chen
Format: Article
Language:English
Published: IEEE 2022-01-01
Series:IEEE Access
Subjects:
Metal surface defect detection
deep learning
YOLO
self-dependent attentive fusion
component randomized Mosaic augmentation
label smoothing
Online Access:https://ieeexplore.ieee.org/document/9870791/

Internet

https://ieeexplore.ieee.org/document/9870791/

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