التفاصيل البيبلوغرافية
| العنوان: |
The prediction results of deep learning algorithm for defect data sets. |
| المؤلفون: |
Yuntao Xu, Peigang Jiao, Jiaqi LIU |
| سنة النشر: |
2023 |
| مصطلحات موضوعية: |
Science Policy, Space Science, Biological Sciences not elsewhere classified, Chemical Sciences not elsewhere classified, Information Systems not elsewhere classified, +aiming%22">xlink "> aiming, standard transformer encoder, metal surface defects, feature extraction ability, experimental results show, greatly improved map, feature enhancement part, improved yolov5 algorithm, improved algorithm, prediction part, reference significance, negative impact, low efficiency, head self, evc module, data set, cfpnet moudle, attention module, analogy experiments, accurately identify, ablation experiments |
| الوصف: |
(a) The traditional YOLOv5 prediction box for defect types. (b) CFM-YOLOv5 algorithm for defect type prediction box. |
| نوع الوثيقة: |
still image |
| اللغة: |
unknown |
| العلاقة: |
https://figshare.com/articles/figure/The_prediction_results_of_deep_learning_algorithm_for_defect_data_sets_/24767228Test |
| DOI: |
10.1371/journal.pone.0289179.g006 |
| الإتاحة: |
https://doi.org/10.1371/journal.pone.0289179.g006Test
https://figshare.com/articles/figure/The_prediction_results_of_deep_learning_algorithm_for_defect_data_sets_/24767228Test |
| حقوق: |
CC BY 4.0 |
| رقم الانضمام: |
edsbas.4C71468B |
| قاعدة البيانات: |
BASE |
