基于改进Faster R CNN的光伏组件红外热斑检测算法

光伏故障检测对光伏电站智能运维具有重要意义。针对光伏组件红外图像中热斑目标小、难检测的问题,研究了基于改进Faster R CNN的光伏组件红外热斑故障检测模型。将Swin Transformer作为Faster R CNN模型中的特征提取模块,捕获图像的全局信息,建立特征之间的依赖关系,提高模型的建模能力；进一步利用BiFPN进行特征融合,改善了热斑故障由于目标小和特征不明显容易被模型忽略掉的问题；同时为了抑制光伏红外图像中背景和噪声的干扰,加入轻量级注意力模块CBAM,使模型更加关注重要通道和关键区域,提高对热斑故障检测精度。在自建光伏组件图像数据集上进行实验,热斑故障检测精度高达915,验证了本文模型对光伏组件热斑故障检测的有效性。

Infrared hot spot detection in photovoltaic modules based on improved Faster R CNN

Photovoltaic fault detection is of great significance to the intelligent operation and maintenance of photovoltaic power plants.To address the problem of small targets and difficult detection of hot spots in infrared images of photovoltaic modules,an ran infrared hot spot fault detection model for PV modules based on improved Faster R CNN is studied.Swin Transformer is employed as the feature extraction module in the Faster R CNN model to capture the global information from the images and establish dependencies between the features,thereby enhancing the modeling capability of the model.Furthermore,the BiFPN is utilized for feature fusion,improving the issue of thermal spot faults that are easily ignored by the model due to the small target and inconspicuous features.Additionally,to suppress interference from background and noise in photovoltaic infrared images,a lightweight attention module called CBAM is incorporated to enable the model to focus more on important channels and key regions,so as to improve the accuracy of thermal spot fault detection.Experimental evaluations are conducted on a self built dataset of photovoltaic component images,resulting in an impressive detection accuracy of 91.5%,which validates the effectiveness of the proposed model for detecting thermal spot faults in photovoltaic components.