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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