EHA-YOLOv5: An Efficient and Highly Accurate Improved YOLOv5 Model for Workshop Bearing Rail Defect Detection Application

EHA-YOLOv5: An Efficient and Highly Accurate Improved YOLOv5 Model for Workshop Bearing Rail Defect Detection Application

Addressing the challenge of surface defect detection in load-bearing rails within auto-motive assembly workshops, which operate in complex environments and under long-term service, this paper proposes an innovative detection framework based on an improved YOLOv5 network. This framework, designed spe...

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Bibliographic Details
Main Authors: Jiyong Hu, Hongfei Yang, Jiatang He, Dongxu Bai, Hongda Chen
Format: Article
Language:English
Published: IEEE 2024-01-01
Series:IEEE Access
Subjects:
YOLOv5
defect detection
dual attention mechanism
residual pyramid pooling model
DBDAMN clustering algorithm
Online Access:https://ieeexplore.ieee.org/document/10552769/
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