Optimizing Automated Optical Inspection: An Adaptive Fusion and Semi-Supervised Self-Learning Approach for Elevated Accuracy and Efficiency in Scenarios with Scarce Labeled Data
In the field of automatic optical inspection (AOI), this study presents innovative strategies to enhance object detection accuracy while minimizing dependence on large annotated datasets. We initially developed a defect detection model using a dataset of 3579 images across 32 categories, created in...
保存先:
| 主要な著者: | , , , , |
|---|---|
| フォーマット: | Artigo |
| 言語: | Inglês |
| 出版事項: |
MDPI AG
2024-09-01
|
| シリーズ: | Sensors |
| 主題: | |
| オンライン･アクセス: | https://www.mdpi.com/1424-8220/24/17/5737 |
| タグ: |
タグなし, このレコードへの初めてのタグを付けませんか!
|