注意力机制和全局卷积在光伏板分割中的应用  

Photovoltaic Panel Segmentation Using Attention Mechanism and Global Convolution

作　　者：李青 李海涛[1] 李辉[1] 张俊虎[1] LI Qing;LI Haitao;LI Hui;ZHANG Junhu(College of Information Science and Technology,Qingdao University of Science and Technology,Qingdao,Shandong 266061,China)

机构地区：[1]青岛科技大学信息科学技术学院,山东青岛266061

出　　处：《计算机工程与应用》2024年第4期237-248,共12页Computer Engineering and Applications

基　　金：国家自然科学基金(61702295)。

摘　　要：准确识别光伏对光伏产业有效健康发展至关重要。高分辨率遥感图像复杂的背景和光伏板形状颜色多变给光伏识别带来巨大的挑战。针对高分辨率遥感图像中光伏用地提取问题,提出网络以精确地提取光伏用地。该网络采用编码器和解码器的形式融合多层特征以结合丰富的语义信息,利用全局卷积和双注意力机制捕获重要的空间特征和通道特征,并使用通道融合模块恢复丢失的部分通道信息。提出的方法可以有效解决光伏板边缘模糊和光伏板粘连的问题。在公开光伏数据集上的实验表明,与U-Net、SegNet、DeepLabv3和DeepLabv3+相比,所提方法在PV01、PV03、PV08三个数据集上的IoU分别达到87.02%、92.98%和88.43%。实验证明所提方法能对高分辨率遥感图像光伏板进行高准确率分割。Accurate photovoltaic(PV)identification is critical for the effective and healthy development of PV industry.PV recognition is hampered by the complex background and variable shape and color of PV panels in high-resolution remote sensing images.This paper proposes a method for accurately extracting photovoltaic land from high-resolution remote sensing images.The encoder and decoder functions in this network combine multi-layer features to combine rich semantic data.Important spatial and channel properties are captured using the global convolution and the dual attention mechanism,while some lost channel data are recovered using the channel fusion module.The proposed method can effectively solve the problems of photovoltaic panel blurred edges and adhesion.Experiments on open PV datasets show that the IoU of the proposed method in PV01,PV03,and PV08 is 87.02%,92.98%,and 88.43%,respectively,when compared to U-Net,SegNet,DeepLabv3,and DeepLabv3+.Experimental results show that the proposed method can achieve high accuracy segmentation of photovoltaic panels in high-resolution remote sensing images.

关 键 词：高分辨率遥感图像 光伏用地 全局卷积 注意力机制 语义分割 

分 类 号：TP751.1[自动化与计算机技术—检测技术与自动化装置]