【摘要】 针对航拍光伏组件红外图像低对比度、背景复杂和热斑小目标难检测的问题，提出一种融合Swin transformer的跨阶段局部网络（CSwin），降低参数量的同时可捕获图像全局位置和空间信息，并以CSwin为基础模块构建多尺度特征路径聚合网络（MPC）加强多尺度特征的信息交互以进一步提高小目标检测能力。对航拍光伏红外图像数据集进行定性和定量实验，证明该方法在航拍光伏红外图像热斑检测任务上的有效性。更多还原

【Abstract】 Aiming at the problems of low contrast,complex background and difficult detection of small hot spots in aerial photovoltaic infrared images,a cross-stage partial network incorporating Swin transformer(CSwin)is proposed to reduce the number of parameters and capture the global position and spatial information of the image. The multi-scale feature path aggregation network based on CSwin(MPC)is constructed to strengthen the information interaction of multi-scale features and further improve the small target detection ability. The qualitative and quantitative experiments are carried out on the aerial photovoltaic infrared image data set, and the effectiveness of the method in aerial photovoltaic infrared image hot spot detection in proved.更多还原