A Photovoltaic Hot-Spot Fault Detection Network for Aerial Images Based on Progressive Transfer Learning and Multiscale Feature Fusion

The number of samples is one of the key factors affecting the performance of deep learning-based detection networks. Aiming at the problem that the detection network is difficult to accurately detect the hot-spot fault targets under the condition of small samples, a photovoltaic hot-spot fault detection network based on progressive transfer learning and multiscale feature fusion is proposed. First, a large number of artificial hot-spot samples are generated through the artificial model, and the mixed dataset containing real and artificial samples is constructed to improve the data diversity. On this basis, a pre-trained model based on artificial samples is established to learn the shallow features of hot-spot faults. Then, to fuse the multiscale features and improve feature aggregation ability of detection network, a novel feature pyramid structure based on reparameterized generalized and multiscale feature fusion (RepG-MSFF) is designed. Moreover, to balance the detection accuracy and speed, the spatial and channel reconstruction convolution (SCConv) is utilized to replace conventional convolution in the backbone network. Finally, to further accurately locate hot-spot targets, an adaptive threshold focal loss (TFL) function is introduced. The experimental results indicate that, in three different scenarios datasets, the detection accuracy can reach 87.9%, 88.6%, and 87.7%, respectively, which is higher than that of other nine detection algorithms.