In order to solve the problem of multi-scale in a single image gathering crowd counting, a new crowd counting network based on the fusion of dilating convolution pyramid and context attention mechanism (DCPCANet) is proposed. With the first ten convolutional layers of VGG16 as the front-end network, an dilated convolutional pyramid fusion attention mechanism module (AMP) is proposed, which is introduced into the three-level upsampling feature fusion module to extract fused multi-scale features, and the AMP module stack is used as the back-end network to capture and fuse multiscale features, The context attention module (CAM) is used to generate the feature map with weight, and high-quality crowd density map is output at the same time. Three mainstream public data sets are adopted, ShanghaiTech PartA,ShanghaiTech PartB,UCF_CC_50. Compared with the previous algorithm, the MAE of the UCF_CC_50 dataset is reduced by 11%, which preliminarily verifies the accuracy and robustness of the model.
KEYWORDS: Solar cells, Defect detection, Detection and tracking algorithms, Feature extraction, Solar energy, Electroluminescence, Silicon, Fuzzy logic, Digital filtering, Corrosion
The detection of micro cracks on the surface of solar cells is very important to improve the durability of photovoltaic modules. In this paper, Haar feature extraction and kernel fuzzy c-means clustering algorithms are proposed to detect the defects of solar cells. Haar extended template is used to extract the edge features as training samples, combined with kernel fuzzy c-means clustering (KFCM) algorithm and improved Xie Beni index to detect the surface defects of solar cells. The recognition rate of no defects is 98%, and the recognition rate of vertical finger defects is 97%, The recognition rate of microcrack is 93%, and that of fracture is 92%.
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