A deep region-based pyramid neural network for automatic detection and multi-classification of various surface defects of aluminum alloys
Aluminum alloys have a wide range of applications in building and civil infrastructure. During the process of production, transportation and storage, various defects inevitably occur on the material, including blisters, scratches, base exposure, dirty points, etc. The efficiency and accuracy of defe...
Saved in:
Main Authors: | Chen, Keyu, Zeng, Zhaoyang, Yang, Jianfei |
---|---|
Other Authors: | School of Electrical and Electronic Engineering |
Format: | Article |
Language: | English |
Published: |
2022
|
Subjects: | |
Online Access: | https://hdl.handle.net/10356/159837 |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Institution: | Nanyang Technological University |
Language: | English |
Similar Items
-
Relationship between fluidity and microstructure of aluminum silicon alloy
by: Tran Duc Huy
Published: (2008) -
Ab-initio study of C and N point defects in the C14-Fe2Nb phase
by: Ladines, Alvin Noe C., et al.
Published: (2017) -
A general strategy towards superhydrophobic self-cleaning and anti-corrosion metallic surfaces : an example with aluminum alloy
by: Zheng, Shunli, et al.
Published: (2021) -
Chemical surface treatment for enhanced bonding strength between polymer coating and aluminum alloy
by: Wu, L.Y.L., et al.
Published: (2014) -
Reversible al metal anodes enabled by amorphization for aqueous aluminum batteries
by: Yan, Chunshuang, et al.
Published: (2022)