Surface defect detection of solar cell based on similarity non-maximum suppression mechanism

Abstract

AbstractThe surface defects such as cracks, broken cells and unsoldered areas on the solar cell caused by manufacturing process defects or artificial operation seriously affect the efficiency of solar cell. For the surface defects of solar cell, which have the characteristics of various shapes, large scale changes, and difficult to detect, a surface defect detection algorithm based on similarity non-maximum suppression mechanism is proposed by improving the Faster region-based convolution neural network (R-CNN) in this paper. In the proposed algorithm, a similarity non-maximum suppression mechanism is used, and the effectiveness of prediction frame screening is improved by introducing the cosine similarity of candidate box aspect-ratio. In addition, the cross-layer connection based on Shuffle operation and the three-branch dilated convolution block are introduced in the main feature extraction channel, which improves the network's ability to express features through multi-scale feature fusion. The experimental results show that, compared with the latest deep learning target detection models, the proposed algorithm not only has higher detection accuracy, but also lower false detection and missed detection rates in various types of defect detection.