KEYWORDS: Solar cells, Defect detection, Performance modeling, Convolution, Object detection, Target detection, Solar energy, Head, Education and training, Data modeling
This study proposes an improved lightweight YOLOv5s neural network model for efficiently identifying various defects on the surface of solar cells. Firstly, ShuffleNetv2 is used as the backbone feature extraction network in the YOLOv5s network. Secondly, the Triplet Attention attention mechanism is introduced into the backbone network of YOLOv5s.Lastly,the two-dimensional activation function FReLU is used to replace Leaky ReLu. The experimental results show that the improved YOLOv5s model has an average accuracy value of 94.1%, ensuring the detection accuracy of defect targets; At the same time, the floating-point operation amount was reduced by 78.7%, and the model size was reduced by 64.8%, effectively improving the model's lightweight performance.
KEYWORDS: Detection and tracking algorithms, Solar cells, Education and training, Defect detection, Radon transform, Visual process modeling, Deep learning, Data modeling, Quantum experiments, Matrices
In response to the lack of publicly available datasets for solar panel defects and the challenges in applying general recognition models, this study presents an algorithm tailored for small-sample solar panel defect detection. The algorithm leverages the strengths of deep learning models and the R-CDT transformation, initially employing Faster R-CNN for defect localization and segmentation, followed by the application of the R-CDT algorithm for defect classification. This approach effectively mitigates human-induced factors and reduces the risk of overfitting in small-sample scenarios. Experimental results demonstrate its high effectiveness as a small-sample recognition method, surpassing popular models (Faster R-CNN and YOLOv5) in terms of recognition accuracy.
The quality of solar panels determines the efficiency of photovoltaic power generation. With the rapid development of the photovoltaic industry, the quality of solar panels has gradually become the focus of the industry. The failure of solar panels limits the photoelectric conversion efficiency and service life of the panels, and poses a huge challenge to the overall safety of the photovoltaic system. Therefore, this article proposes a solar panel fault diagnosis method based on the YOLOv3 algorithm. The algorithm optimizes the learning rate configuration, the determination of the optimal anchor frame, and the avoidance of identifying multiple anchor frame parts on the basis of the YOLOv3 algorithm. And it can detect many different types of target failure points at the same time. The experimental results verify the effectiveness of the algorithm.
The aim of multimodal image fusion is to enhance the perception of a scene by combining prominent features of images captured by different sensors. Using joint sparse subspace recovery (JSSR), this paper proposes an image fusion method. We consider each source image as projecting the original scene into a specified low-dimensional subspace that can be learned by the orthogonal matching pursuit (OMP) algorithm. We then reconstruct the fused image from a union of these subspaces. Considering the high computational complexity of the OMP algorithm, we provide an optimized OMP implementation for a large set of signals on the same dictionary. We evaluate the proposed JSSR fusion method on different spectral images, and compare its performance with the other state-of-the-art methods in terms of visual effect and quantitative fusion evaluation metrics. The experimental results demonstrate that our approach can enhance the visual quality of the fused images.
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