Detection of wind turbine towers in remote sensing based on YOLOv3 model under scale and density constraints
-
摘要: 风电场分布是风电投资监测预警、占地监测和清洁能源消纳能力评价的重要依据,卫星遥感技术是大范围提取风电场分布信息的有效方法。风电塔架作为风电场的遥感解译标识,其在高分影像中是一种多尺度目标,且受影像获取时间、光照条件、地表覆盖等影响导致特征差异大,遥感自动检测难度大。针对以上问题,提出一种尺度和密度约束下基于YOLOv3模型的风电塔架自动检测方法。首先,在风电场遥感特征分析基础上,确定样本构建条件,分析风电塔架目标尺度; 然后,压缩YOLOv3模型特征提取网络以提高多尺度目标特征表征能力,并将目标尺寸作为先验知识输入模型; 最后,基于噪声与风电塔架的密度差异,采用DBSCAN密度聚类算法抑制误检。实验结果表明,该方法在典型试验区取得的风电塔架检测准确率为96%,召回率为94%,F1为95%,效果优于Faster R-CNN和FPN等基准模型,表明本文方法对于遥感影像复杂背景下的小目标具有良好的检测效果。Abstract: The distribution of wind farms is an important basis for the monitoring and early warning of wind power investment, the analyses of land occupation, and the assessment of clean energy consumption capacity. Remote sensing technology serves as an effective method for extracting wind farm distribution on a large scale. As the remote sensing interpretation marks of wind farms, wind turbine towers are a kind of multi-scale targets in high-resolution images. However, their characteristics greatly differ due to the effects of image acquisition time, illumination conditions, and surface coverage. Therefore, it's difficult to automatically detect wind turbine towers in remote sensing images. Aiming at the above problems, this paper proposed an automatic detection method of wind turbine towers based on the YOLOv3 model, and the steps are as follows. Firstly, determine the sample construction conditions and the target scale of wind turbine towers according to the analyses of the remote sensing characteristics of a wind farm. Secondly, optimize the depth of the feature extraction network of the YOLOv3 model to improve the characterization capacity of multi-scale targets. Finally, suppress false detections using the DBSCAN density clustering algorithm according to the density difference between noise and wind turbine towers. The experimental results show that the proposed method exhibits superiority over the benchmark models such as Faster R-CNN and FPN. With a detection accuracy rate of 96%, a recall rate of 94%, and F1 of 95%, the proposed method has good effects for the detection of small targets in the remote sensing images with complex background.
-
Key words:
- object detection /
- YOLOv3 /
- DBSCAN /
- wind turbine tower /
- remote sensing /
-
-
[1] Chen J B, Yue A Z, Wang C Y, et al. Wind turbine extraction from high spatial resolution remote sensing images based on saliency detection[J]. Journal of Applied Remote Sensing, 2018, 12(1):016041-016041.
[2] 于野, 艾华, 贺小军, 等. A-FPN算法及其在遥感图像船舶检测中的应用[J]. 遥感学报, 2020, 24(2):107-115.
[3] Yu Y, Ai H, He X J, et al. Attention-based feature pyramid networks for ship detection of optical remote sensing image[J]. Journal of Remote Sensing, 24(2):107-115.
[4] 董彪, 熊风光, 韩燮, 等. 基于改进Yolo v3算法的遥感建筑物检测研究[J]. 计算机工程与应用, 2020, 56(18):209-213.
[5] Dong B, Xiong F G, Han X, et al. Research on remote sensing building detection based on improved Yolo v3 algorithm[J]. Computer Engineering and Applications, 2020, 56(18):209-213.
[6] 赵文博. 基于卷积神经网络的风力发电站遥感检测及其空间分布研究[D]. 广州:华南师范大学, 2019.
[7] Zhao W B. Detection and spatial distribution of wind turbine based on convolutional neural network[D]. Guangzhou:South China Normal University, 2019.
[8] Redmon J, Farhadi A. YOLOv3:An incremental improvement[J]. arXiv,2018:arXiv:1804.02767.
[9] 张鹏. 基于卷积神经网络的光学遥感图像中机场目标识别研究[D]. 北京:国防科学技术大学, 2016.
[10] Zhang P. Airport detection in optical remote sensing images with convolution neural network[D]. Beijing:National University of Defense Technology, 2016.
[11] 韩志华. 基于统计特征与桥梁方法的小目标检测算法研究[D]. 长春:中国科学院长春光学精密机械与物理研究所, 2019.
[12] Han Z H. Research on small target detection algorithm based on statistical features and bridge method[D]. Changchun:Changchun Institute of Optics,Fine Mechanics and Physics,Chinese Academy of Sciences, 2019.
[13] Barret Z, Ekin D C, Golnaz G, et al. Learning data augmentation strategies for object detection:America,US2019354817[P]. 2019-11-21.
[14] 张翠, 邹涛, 王正志. 一种高分辨率SAR图像快速目标检测算法[J]. 遥感学报, 2005, 9(1):45-49.
[15] Zhang C, Zou T, Wang Z Z. A fast target detection algorithm for high resolution SAR imagery[J]. Journal of Remote Sensing, 2005, 9(1):45-49.
[16] 张荫华, 杨萌. SAR图像舰船目标检测的信息几何方法[J]. 中国图象图形学报, 2020, 25(1):206-213.doi: 10.11834/jig.190131.
[17] Zhang Y H, Yang M. Information geometry method for ship detection in SAR images[J]. Journal of Image and Graphics, 2020, 25(1):206-213.doi: 10.11834/jig.190131.
[18] 谢奇芳, 姚国清, 张猛. 基于Faster R-CNN的高分辨率图像目标检测技术[J]. 国土资源遥感, 2019, 31(2):38-43.doi: 10.6046/gtzyyg.2019.02.06.
[19] Xie Q F, Yao G Q, Zhang M. Research on high resolution image object detection technology based on Faster R-CNN[J]. Remote Sensing for Land and Resources, 2019, 31(2):38-43.doi: 10.6046/gtzyyg.2019.02.06.
[20] 郑志强, 刘妍妍, 潘长城, 等. 改进YOLOV3遥感图像飞机识别应用[J]. 电光与控制, 2019, 26(4):28-32.doi: 10.3969/j.issn.1671-637X.2019.04.006.
[21] Zhang Z Q, Liu Y Y, Pan C C, et al. Application of improved YOLO V3 in aircraft recognition of remote sensing images[J]. Electronics Optics and Control, 2019, 26(4):28-32.doi: 10.3969/j.issn.1671-637X.2019.04.006.
[22] 谌华. SAR图像目标自动检测与识别方法研究[D]. 北京:中国科学院国家空间科学中心, 2019.
[23] Chen H. A study on methods of SAR image target automatic detection and recognition[D]. Beijing:National Space Science Center,Chinese Academy of Sciences, 2019.
[24] 吴柳青. 基于多尺度多特征融合的遥感影像建筑物自动提取[D]. 武汉:武汉大学, 2018.
[25] Wu L Q. Automatic building extraction of remote sensing images based on fused multi-scale and multi-feature[D]. Wuhan:Wuhan University, 2018.
[26] 申原. 基于深度学习的遥感影像目标检测算法研究[D]. 深圳:中国科学院深圳先进技术研究院, 2020.
[27] Shen Y. Research on object detection algorithm of remote sensing images based on deep learning[D]. Shenzhen:Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences, 2020.
[28] Ren S Q, He K M, Girshick R, et al. Faster R-CNN:Towards real-time object detection with region proposal networks[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2017, 39(6):1137-1149.
[29] Xie Y, Cai J, Bhojwani R, et al. A locally-constrained YOLO framework for detecting small and densely-distributed building footprints[J]. International Journal of Geographical Information Science, 2020, 34(4):777-801.
[30] Wu Z, Chen X, Gao Y, et al. Rapid target detection in high resolution remote sensing images using YOLO model[J]. International Archives of the Photogrammetry,Remote Sensing and Spatial Information Sciences, 2018, 42:3.
[31] 陈慧元, 刘泽宇, 郭炜炜, 等. 基于级联卷积神经网络的大场景遥感图像舰船目标快速检测方法[J]. 雷达学报, 2019, 8(3):413-424.doi: 10.12000/JR19041.
[32] Chen H Y, Liu Z Y, Guo W W, et al. Fast detection of ship targets for large-scale remote sensing image based on a cascade convolutional neural network[J]. Journal of Radars, 2019, 8(3):413-424.doi: 10.12000/JR19041.
-
计量
- 文章访问数: 920
- PDF下载数: 138
- 施引文献: 0
下载: