English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Object detection plays an important role in many computer vision applications. Innovative object detection methods based on deep learning such as Faster R-CNN, YOLO, and SSD have achieved state-of the-art results in terms of detection accuracy. There have been few studies to date on object detection with the addition of new classes, however, though this problem is often encountered in the industry. Therefore, this issue has important research significance and practical value. On the premise that the old class samples are available, a method of reserving nodes in advance in the output layer (RNOL) was established in this study. Experiments show that RNOL can achieve high detection accuracy in both new and old classes over a short training time while outperforming the traditional fine-tuning method.

Object Detection with the Addition of New Classes Based on the Method of RNOL