DSFA-SwinNet: A Multi-Scale Attention Fusion Network for Photovoltaic Areas Detection - Shaofu Lin; Yang Yang; Xiliang Liu; Li Tian (Remote Sensing)
Precise statistics on the spatial distribution of photovoltaics (PV) are essential for advancing the PV industry, and integrating remote sensing with artificial intelligence technologies offers a robust solution for accurate identification. Currently, numerous studies focus on the detection of single-type PV installations through aerial or satellite imagery. However, due to the variability in scale and shape of PV installations in complex environments, the detection results often fail to capture detailed information and struggle to scale for multi-scale PV systems. To tackle these challenges, a detection method known as Dynamic Spatial-Frequency Attention SwinNet (DSFA-SwinNet) for multi-scale PV areas is proposed. First, this study proposes the Dynamic Spatial-Frequency Attention (DSFA) mechanism, the Pyramid Attention Refinement (PAR) bottleneck structure, and optimizes the feature propagation method to achieve dynamic decoupling of the spatial and frequency domains in multi-scale representation learning. Secondly, a hybrid loss function has been developed with weights optimized employing the Bayesian Optimization algorithm to provide a strategic method for parameter tuning in similar research. Lastly, the fixed window size of Swin-Transformer is dynamically adjusted to enhance computational efficiency and maintain accuracy. The results on two PV datasets demonstrate that DSFA-SwinNet significantly enhances detection accuracy and scalability for multi-scale PV areas.