基于散点图-AlexNet网络的光伏红外热图像识别方法

PHOTOVOLTAIC INFRARED THERMAL IMAGE RECOGNITION METHOD BASED ON SCATTER PLOT-AlexNet NETWORK

针对神经网络在光伏发电中对光伏红外热图像识别准确率和训练效率低的问题,提出基于散点图-AlexNet网络的识别模型。首先将光伏红外热像图对应的HSI空间数据信息直接投射到平面坐标系中,形成由若干散点构成的可视化RGB三色图像,并使用基于AlexNet网络的快速AlexNet网络进行训练。为了证明新方法的优良性能,选取识别准确率和单次训练时间对模型进行评价。实验结果表明,该方法识别率高,能准确识别光伏红外热图像中的图片种类,且训练效率也显著提升。

Aiming at the low recognition accuracy and training efficiency of neural network for photovoltaic infrared thermal image in photovoltaic generation,a recognition model based on scatter plot-AlexNet network is proposed. First,the HSI spatial data information corresponding to the photovoltaic infrared thermal image is directly projected into the plane coordinate system to form a visual RGB three-color image composed of several scattered points,and the rapid AlexNet network based on the AlexNet network is used for training. In order to prove the excellent performance of the new method,the recognition accuracy and single training time are selected to evaluate the model. Experimental results show that this method has high recognition rate,can accurately identify the types of images in photovoltaic infrared thermal images,and the training efficiency is significantly improved.