针对光伏热斑识别算法中存在的深层网络参数运算复杂、梯度信息易消失和模型退化准确率下降等问题,提出一种基于特征金字塔融合高分辨率网络的光伏热斑识别检测算法。首先,该算法搭建一种多分辨率子网并行连接的网络模型,解决深层网络热斑细节信息丢失、特征冗余的难题。其次,引入特征金字塔的多尺度融合模块,跨层连接深浅层不同尺度特征图,解决特征语义的鸿沟、提高模型识别精度。实验结果表明：所提出的算法在光伏红外热斑图像数据集上的分类效果优于经典的深度卷积神经网络算法,准确率可达97.2%,可实现高精度高分辨率的热斑检测识别。
Abstract
Aiming at the problems existing in the photovoltaic hot spot recognition algorithm, such as the complex calculation of deep network parameters, the easy disappearance of gradient information and the reduced accuracy of model degradation, a photovoltaic hot spot identification and detection algorithm based on feature pyramid fusion high-resolution network is proposed. Firstly, a network model with parallel connection of multi-resolution subnetworks is build in this algorithm, which solves the problems of loss of detailed information and redundant features of hot spots in deep networks. Secondly, the multi-scale fusion module of the feature pyramid is introduced, which connects the feature maps of different scales across layers, solves the feature semantic gap, and improves the accuracy of model recognition. The experimental results show that the classification effect of the proposed algorithm on the photovoltaic infrared hot spot image dataset is better than the classical deep convolutional neural network algorithm, and the accuracy rate can reach 97.2%. The algorithm realizes high-precision and high-resolution hot spot detection and identification.
关键词
光伏效应 /
特征提取 /
图像分类 /
高分辨率网络 /
热斑
Key words
photovoltaic effect /
feature extraction /
image classification /
high-resolution network /
hot spot
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