Global contextual attention augmented YOLO with ConvMixer prediction heads for PCB surface defect detection.
Sci Rep
; 13(1): 9805, 2023 Jun 16.
Article
de En
| MEDLINE
| ID: mdl-37328545
ABSTRACT
To solve the problem of missed and false detection caused by the large number of tiny targets and complex background textures in a printed circuit board (PCB), we propose a global contextual attention augmented YOLO model with ConvMixer prediction heads (GCC-YOLO). In this study, we apply a high-resolution feature layer (P2) to gain more details and positional information of small targets. Moreover, in order to suppress the background noisy information and further enhance the feature extraction capability, a global contextual attention module (GC) is introduced in the backbone network and combined with a C3 module. Furthermore, in order to reduce the loss of shallow feature information due to the deepening of network layers, a bi-directional weighted feature pyramid (BiFPN) feature fusion structure is introduced. Finally, a ConvMixer module is introduced and combined with the C3 module to create a new prediction head, which improves the small target detection capability of the model while reducing the parameters. Test results on the PCB dataset show that GCC-YOLO improved the Precision, Recall, mAP@0.5, and mAP@0.50.95 by 0.2%, 1.8%, 0.5%, and 8.3%, respectively, compared to YOLOv5s; moreover, it has a smaller model volume and faster reasoning speed compared to other algorithms.
Texte intégral:
1
Collection:
01-internacional
Base de données:
MEDLINE
Sujet principal:
Rappel mnésique
/
Algorithmes
Type d'étude:
Diagnostic_studies
/
Prognostic_studies
/
Risk_factors_studies
Langue:
En
Journal:
Sci Rep
Année:
2023
Type de document:
Article
Pays d'affiliation:
Chine