.png){kind=icon}
.png){kind=icon}
Return
Deep Learning-Based Solar-Cell Manufacturing Defect Detection With Complementary Attention Network
.png){kind=icon}
PRE
AI
DOI：10.1109/TII.2020.3008021.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
The automatic defects detection for solar cell electroluminescence (EL) images is a challenging task, due to the similarity of defect features and complex background features. To address this problem, in this article a novel complementary attention network (CAN) is designed by connecting the novel channel-wise attention subnetwork with spatial attention subnetwork sequentially, which adaptively suppresses the background noise features and highlights the defect features simultaneously by employing the complementary advantage of the channel features and spatial position features. In CAN, the novel channel-wise attention subnetwork applies convolution operation to integrate the concatenated and discriminative output features extracted by global average pooling layer and global max pooling layer, which can make fully use of these informative features. Furthermore, a region proposal attention network (RPAN) is proposed by embedding CAN into region proposal network in faster R-CNN (convolution neutral network) to extract more refined defective region proposals, which is used to construct a novel end-to-end faster RPAN-CNN framework for detecting defects in raw EL image. Finally, some experimental results on a large-scale EL dataset including 3629 images, 2129 of which are defective, show that the proposed method performs much better than other methods in terms of defects classification and detection results in raw solar cell EL images.
Keywords:
Photovoltaic cells
Feature extraction
Proposals
Task analysis
Shape
Convolution
Visualization
Attention network
automatic defects detection
near-infrared image
region proposal network (RPN)
solar cell
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
9.9
Papers:
8.3K
Citations:
6.0W