![]() To overcome this challenge, motivated by ensemble learning that uses multiple learning algorithms to obtain better predictive performance, we develop an ensemble framework for industrialized rail defect detection. Selecting an appropriate model and tuning it with insufficient annotated rail defect images is time-consuming and tedious. However, the CNN-based detection models are diverse from each other in performance, and most of them require sufficient training samples to achieve high detection performance. The CNN-based computer vision approach has been proved to be a strong detection tool widely used in various industrial scenarios. ![]() The detection of rail surface defects is vital for high-speed rail maintenance and management.
0 Comments
Leave a Reply. |
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |