融合视觉特征的光伏组件语义分割模型研究

王银; 沈灵鑫; 李茂环; 王健安; 李小松

doi:10.19912/j.0254-0096.tynxb.2023-0265

PDF(5707 KB)

太阳能学报 ›› 2024, Vol. 45 ›› Issue (4) : 500-511. DOI: 10.19912/j.0254-0096.tynxb.2023-0265

融合视觉特征的光伏组件语义分割模型研究

王银¹, 沈灵鑫¹, 李茂环², 王健安¹, 李小松¹

作者信息 +

SEMANTIC SEGMENTATION MODEL OF PHOTOVOLTAIC MODULES BASED ON VISUAL FEATURES

Wang Yin¹, Shen Lingxin¹, Li Maohuan², Wang Jian'an¹, Li Xiaosong¹

Author information +

文章历史 +

摘要

针对光伏组件红外图像的分割问题,使用MobileNetv2作为DeepLabv3+的主干特征提取网络并使用位置通道注意力模块减少背景干扰,引入混合条带池化对ASPP模块进行优化,帮助模型进一步捕获全局和上下文信息。针对检测困难的屋顶光伏组件设计DeepLabv3-T网络,在上述改进的基础上融入纹理信息进行选择性背景抑制,实现光伏组件的精确分割。在PV_large和PV_roof数据集上进行实验证明该文方法优于现有技术,DeepLabv3-T相较于DeepLabv3+,mIoU值分别提高了2.74%和10.53%。此外,设计消融实验表明各个改进模块的有效性。

Abstract

To solve the segmentation problem of infrared images of photovoltaic modules, MobileNetv2 is used as the backbone feature extraction network of DeepLabv3+ and the location channel attention module is used to reduce background interference. Mixed strip pooling is introduced to optimize the ASPP module, which helps the model to further capture global and contextual information. The DeepLabv3-T network is designed for rooftop PV modules with difficult detection. Based on the above improvements, texture information is incorporated into the selective background suppression to achieve accurate segmentation of PV modules. Experimental results on the PV_large and PV_roof datasets demonstrate that the text-based approach is superior to the prior art, and the mIoU value of deeplabv3-t is 2.74% and 7.93% higher than that of DeepLabv3+, respectively. In addition, ablation experiments are designed to demonstrate the effectiveness of each improved module.

导出引用

王银, 沈灵鑫, 李茂环, 王健安, 李小松. 融合视觉特征的光伏组件语义分割模型研究[J]. 太阳能学报. 2024, 45(4): 500-511 https://doi.org/10.19912/j.0254-0096.tynxb.2023-0265

Wang Yin, Shen Lingxin, Li Maohuan, Wang Jian'an, Li Xiaosong. SEMANTIC SEGMENTATION MODEL OF PHOTOVOLTAIC MODULES BASED ON VISUAL FEATURES[J]. Acta Energiae Solaris Sinica. 2024, 45(4): 500-511 https://doi.org/10.19912/j.0254-0096.tynxb.2023-0265

中图分类号： TP391.4

参考文献

[1] GIELEN D, BOSHELL F, SAYGIN D, et al.The role of renewable energy in the global energy transformation[J]. Energy strategy reviews, 2019, 24: 38-50.
[2] HANSEN K, BREYER C, LUND H.Status and perspectives on 100% renewable energy systems[J]. Energy, 2019, 175: 471-480.
[3] 杨子龙, 王一波. 光伏发电系统测控技术研究[J]. 太阳能学报, 2015, 36(4): 1023-1028.
YANG Z L, WANG Y B.Research on measurement and control technology of photovoltaic power generation system[J]. Acta energiae solaris sinica, 2015, 36(4): 1023-1028.
[4] 陈波. 太阳能光伏发电及屋顶光伏电站的安装[J]. 光源与照明, 2022(5): 122-124.
CHEN B.Solar photovoltaic power generation and installation of roof photovoltaic power station[J]. Lamps & lighting, 2022(5): 122-124.
[5] 位硕权. 基于红外图像的光伏组件热斑智能检测[D]. 杭州: 浙江大学, 2020.
WEI S Q.Intelligent detection of hotspots of photovoltaic modules based on infrared images[D]. Hangzhou: Zhejiang University, 2020.
[6] HU Y H, CAO W P, MA J E, et al.Identifying PV module mismatch faults by a thermography-based temperature distribution analysis[J]. IEEE transactions on device and materials reliability, 2014, 14(4): 951-960.
[7] 丁世浩. 基于计算机视觉的光伏组件缺陷诊断研究[D]. 杭州: 浙江大学, 2020.
DING S H.Research on defect diagnosis of photovoltaic modules based on computer vision[D]. Hangzhou:Zhejiang University, 2020.
[8] 李琼, 吴文宝, 刘斌, 等. 基于迁移学习的光伏组件鸟粪覆盖检测[J]. 太阳能学报, 2022, 43(2): 233-237.
LI Q, WU W B, LIU B, et al.Bird droppings coverage detection of photovoltaic module based on transfer learning[J]. Acta energiae solaris sinica, 2022, 43(2): 233-237.
[9] 周颖, 叶红, 王彤, 等. 基于多尺度CNN的光伏组件缺陷识别[J]. 太阳能学报, 2022, 43(2): 211-216.
ZHOU Y, YE H, WANG T, et al.Photovoltaic module defect identification based on mulit-scale convolution neural network[J]. Acta energiae solaris sinica, 2022, 43(2): 211-216.
[10] 王道累, 李明山, 姚勇, 等. 改进SSD的光伏组件热斑缺陷检测方法[J]. 太阳能学报, 2023, 44(4): 420-425.
WANG D L, LI M S, YAO Y, et al.Method of hotspot detection of photovoltaic panels modules on improved ssd[J]. Acta energiae solaris sinica, 2023, 44(4): 420-425.
[11] 孙海蓉, 李帆. 基于注意力机制的光伏热斑识别[J]. 太阳能学报, 2023, 44(2): 453-459.
SUN H R, LI F.Photovoltaic hot spot recognition based on attention mechanism[J]. Acta energiae solaris sinica, 2023, 44(2): 453-459.
[12] KAMILARIS A, PRENAFETA-BOLDÚ F X. Deep learning in agriculture: a survey[J]. Computers and electronics in agriculture, 2018, 147: 70-90.
[13] 刘瑞军, 王向上, 张晨, 等. 基于深度学习的视觉SLAM综述[J]. 系统仿真学报, 2020, 32(7): 1244-1256.
LIU R J, WANG X S, ZHANG C, et al.A survey on visual SLAM based on deep learning[J]. Journal of system simulation, 2020, 32(7): 1244-1256.
[14] 罗荣, 王亮, 肖玉杰. 深度学习技术应用现状分析与发展趋势研究[J]. 计算机教育, 2019(10): 19-22.
LUO R, WANG L, XIAO Y J.Analysis on application status and development trend of deep learning technology[J]. Computer education, 2019(10): 19-22.
[15] ZHANG F, CHEN Y Q, LI Z H, et al.ACFNet: attentional class feature network for semantic segmentation[C]//2019 IEEE/CVF International Conference on Computer Vision (ICCV). Seoul, Korea, 2019: 6797-6806.
[16] ZHAO Y C, LIU S G, HU Z H.Focal learning on stranger for imbalanced image segmentation[J]. IET image processing, 2022,16(5): 1305-1323.
[17] ZHAO Y C, LIU S G, HU Z H.Dynamically balancing class losses in imbalanced deep learning[J]. Electronics letters, 2022, 58(5): 203-206.
[18] SHELHAMER E, LONG J, DARRELL T.Fully convolutional networks for semantic segmentation[C]//IEEE Transaction on Pattern Analysis and Machine Inteuigence, Boston, MA, USA, 2017: 640-651.
[19] GUO Y M, LIU Y, GEORGIOU T, et al.A review of semantic segmentation using deep neural networks[J]. International journal of multimedia information retrieval, 2018, 7(2): 87-93.
[20] BADRINARAYANAN V, KENDALL A, CIPOLLA R.SegNet: a deep convolutional encoder-decoder architecture for image segmentation[J]. IEEE transactions on pattern analysis and machine intelligence, 2017, 39(12): 2481-2495.
[21] 李紫薇, 英昌盛, 于晓鹏, 等. 基于改进SegNet模型的遥感图像建筑物分割[J]. 吉林大学学报(理学版), 2022, 60(2): 409-416.
LI Z W, YING C S, YU X P, et al.Building segmentation of remote sensing image based on improved SegNet model[J]. Journal of Jilin University (science edition), 2022, 60(2): 409-416.
[22] RONNEBERGER O, FISCHER P, BROX T.U-net: convolutional networks for biomedical image segmentation[C]//International Conference on Medical Image Computing and Computer-Assisted Intervention. Cham: Springer, 2015: 234-241.
[23] SCHONFELD E, SCHIELE B, KHOREVA A.A U-net based discriminator for generative adversarial networks[C]//2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). Seattle, WA, USA, 2020: 8204-8213.
[24] JAEGER P, KOHL S A A, BICKELHAUPT S, et al. Retina U-net: embarrassingly simple exploitation of segmentation supervision for medical object detection[C]//Proceedings of the Machine Learning for Health Workshop, Shangri-La, China, 2018.
[25] 王丽芳, 米嘉, 秦品乐, 等. 改进U-Net3+与跨模态注意力块的医学图像融合[J]. 中国图象图形学报, 2022, 27(12): 3622-3636.
WANG L F, MI J, QIN P L, et al.Medical image fusion using improved U-Net3+ and cross-modal attention blocks[J]. Journal of image and graphics, 2022, 27(12): 3622-3636.
[26] 魏欣, 李锵, 关欣. 基于3D U-Net的轻量级脑肿瘤分割网络[J]. 光电子·激光, 2022, 33(12): 1338-1344.
WEI X, LI Q, GUAN X.Lightweight network in brain tumor segmentation based on 3D U-Net[J]. Journal of optoelectronics·laser, 2022, 33(12): 1338-1344.
[27] CHEN L C, PAPANDREOU G, KOKKINOS I,et al.Semantic image segmentation with deep convolutional nets and fully connected CRFs[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, Washington, USA, 2014.
[28] SIMONYAN K, ZISSERMAN A.Very deep convolutional networks for large-scale image recognition[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, Washington, USA, 2014.
[29] CHEN L C, PAPANDREOU G, KOKKINOS I, et al.DeepLab: semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected CRFs[J]. IEEE transactions on pattern analysis and machine intelligence, 2018, 40(4): 834-848.
[30] CHEN L C, PAPANDREOU G, SCHROFF F, et al.Rethinking atrous convolution for semantic image segmenta tion[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA, 2017.
[31] CHEN L C, ZHU Y K, PAPANDREOU G, et al.Encoder-decoder with atrous separable convolution for semantic image segmentation[C]//Computer Vision-ECCV 2018: 15th European Conference. Munich, Germany, 2018: 833-851.
[32] OJALA T, PIETIKÄINEN M, MÄENPÄÄ T. Multiresolution gray-scale and rotation invariant texture classification with local binary patterns[J]. IEEE transactions on pattern analysis and machine intelligence, 2002, 24(7): 971-987.
[33] VON GIOI R G, JAKUBOWICZ J, MOREL J M, et al. LSD: a fast line segment detector with a false detection control[J]. IEEE transactions on pattern analysis and machine intelligence, 2010, 32(4): 722-732.
[34] 王华俊, 葛小三. 一种轻量级的DeepLabv3+遥感影像建筑物提取方法[J]. 自然资源遥感, 2022, 34(2): 128-135.
WANG H J, GE X S.Lightweight DeepLabv3+ building extraction method from remote sensing images[J]. Remote sensing for natural resources, 2022, 34(2): 128-135.
[35] TIAN D P.A review on image feature extraction and representation techniques[J]. International journal of multimedia and ubiquitous engineering, 2013, 8(4): 385-395.