A Photovoltaic Hot-Spot Fault Detection Network for Aerial Images Based on Progressive Transfer Learning and Multiscale Feature Fusion
Shuai Hao, Jiahao Li, Xu Ma, Siya Sun, Zhuo Tian, Tianqi Li, Yifeng Hou · IEEE Transactions on Geoscience and Remote Sensing · 2024
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.