automatic detection of transformer components in inspection images based on improved faster r-cnn
Clicks: 300
ID: 140573
2018
Article Quality & Performance Metrics
Overall Quality
Not rated
Combines reader engagement with the AI quality analysis. This
article has not been analysed, so there is no overall score —
reader engagement is measured and shown alongside.
Reader Engagement
Steady Performance
30.0
/100
300 views
56 readers
AI Quality Assessment
Not analyzed
Readership in this journal
SteadyRanked #50 of 526 articles by views in acs combinatorial science
Most read
Least read
Bar heights use a square-root scale. Only the 120 most-read articles are drawn; the journal has 526 in total.
Mint this article as an NFT
Not yet mintedCreate a permanent, verifiable on-chain record of this article on the Scimatic Network. The NFT is held in your Journament account, and you can withdraw it to your own wallet at any time.
5
SUSD
one-off · no wallet required
Abstract
To detect the categories and positions of various transformer components in inspection images automatically, this paper proposes a transformer component detection model with high detection accuracy, based on the structure of Faster R-CNN. In consideration of the significant difference in component sizes, double feature maps are used to adapt to the size change, by adjusting two weights dynamically according to the object size. Moreover, different from the detection of ordinary objects, there is abundant useful information contained in the relative positions between components. Thus, the relative position features are defined and introduced to the refinement of the detection results. Then, the training process and detection process are proposed specifically for the improved model. Finally, an experiment is given to compare the accuracy and efficiency of the improved model and the original Faster R-CNN, along with other object detection models. Results show that the improved model has an obvious advantage in accuracy, and the efficiency is significantly higher than that of manual detection, which suggests that the model is suitable for practical engineering applications.
| Reference Key |
liu2018energiesautomatic
Use this key to autocite in the manuscript while using
SciMatic Manuscript Manager or Thesis Manager
|
|---|---|
| Authors | ;Ziquan Liu;Huifang Wang |
| Journal | acs combinatorial science |
| Year | 2018 |
| DOI |
10.3390/en11123496
|
| URL | |
| Keywords |
Citations
No citations found. To add a citation, contact the admin at info@scimatic.org
Comments
No comments yet. Be the first to comment on this article.