YOT-Net: YOLOv3 Combined Triplet Loss Network for Copper Elbow Surface Defect Detection.
Sensors (Basel)
; 21(21)2021 Oct 31.
Article
en En
| MEDLINE
| ID: mdl-34770569
ABSTRACT
Copper elbows are an important product in industry. They are used to connect pipes for transferring gas, oil, and liquids. Defective copper elbows can lead to serious industrial accidents. In this paper, a novel model named YOT-Net (YOLOv3 combined triplet loss network) is proposed to automatically detect defective copper elbows. To increase the defect detection accuracy, triplet loss function is employed in YOT-Net. The triplet loss function is introduced into the loss module of YOT-Net, which utilizes image similarity to enhance feature extraction ability. The proposed method of YOT-Net shows outstanding performance in copper elbow surface defect detection.
Palabras clave
Texto completo:
1
Colección:
01-internacional
Base de datos:
MEDLINE
Asunto principal:
Algoritmos
/
Cobre
Tipo de estudio:
Diagnostic_studies
Idioma:
En
Revista:
Sensors (Basel)
Año:
2021
Tipo del documento:
Article
País de afiliación:
China