Detecting land for photovoltaic development based on the attention mechanism and improved YOLOv5

doi:10.6046/zrzyyg.2022315

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Abstract

In response to the detection and positioning demands for land for photovoltaic development due to the rapid growth of the photovoltaic industry, this study proposed a YOLOv5-pv algorithm for the detection of land for photovoltaic development based on the improved YOLOv5. For quick and accurate detection and positioning of land for photovoltaic development in complex scenes, the YOLOv5-pv algorithm adopted a weighted bi-directional feature pyramid based on YOLOv5 to achieve simple and fast multi-scale feature fusion, thereby enhancing the ability to detect small targets. Subsequently, the Ghost convolution was employed to retain valuable feature map information in redundant information. Finally, a co-attention mechanism was integrated to improve the algorithm's attention on the land for photovoltaic development, increasing its capacity to resist background interference. The experimental results demonstrate that YOLOv5-pv outperformed YOLOv5, with the recall rate and average accuracy improved by 6.68 percentage points and 4.43 percentage points, respectively. Therefore, the method proposed in this study can effectively detect the land for photovoltaic development, holding referential significance for relevant detection research.

Keywords deep learning YOLOv5 land for photovoltaic development remote sensing imaging detection attention mechanism

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TP79

Issue Date: 21 December 2023

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	Di CHEN
	Qiuzhi PENG
	Peiyi HUANG
	Yaxuan LIU

Cite this article:

Di CHEN,Qiuzhi PENG,Peiyi HUANG, et al. Detecting land for photovoltaic development based on the attention mechanism and improved YOLOv5[J]. Remote Sensing for Natural Resources, 2023, 35(4): 90-95.

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https://www.gtzyyg.com/EN/10.6046/zrzyyg.2022315 OR https://www.gtzyyg.com/EN/Y2023/V35/I4/90

Fig.1 Bi-directional feature pyramid network

Fig.2 Traditional convolution and Ghost convolution

Fig.3 YOLOv5-pv algorithm

Tab.1 Mean average precision of improved algorithm

Tab.2 Test results of two algorithms

Tab.3 Comparison of test results

[1]	Hernandez R R, Hoffacker M K, Field C B. Land-use efficiency of big solar[J]. Environmental Science & Technology, 2014, 48(2):1315-1323. doi: 10.1021/es4043726 url: https://pubs.acs.org/doi/10.1021/es4043726
[2]	韩梦瑶, 熊焦, 刘卫东. 中国光伏发电的时空分布、竞争格局及减排效益[J]. 自然资源学报, 2022, 37(5):1338-1351. doi: 10.31497/zrzyxb.20220516
[2]	Han M Y, Xiong J, Liu W D, Spatio-temporal distribution,competitive development and emission reduction of China’s photovoltaic power generation[J]. Journal of Natural Resources, 2022, 37 (5):1338-1351. doi: 10.31497/zrzyxb.20220516 url: http://www.jnr.ac.cn/EN/10.31497/zrzyxb.20220516
[3]	王胜利, 张连蓬, 朱寿红, 等. 多共性特征联合的Landsat8 OLI遥感影像光伏电站提取[J]. 测绘通报, 2018,(11):46-52. doi: 10.13474/j.cnki.11-2246.2018.0348
[3]	Wang S L, Zhang L P, Zhu S H, et al. et al Multi-invariant feature combined photovoltaic power plants extraction using multi-temporal Landsat8 OLI imagery[J]. Surveying and Mapping Bulletin, 2018,(11):46-52.
[4]	Zhang X, Zeraatpisheh M, Rahman M M, et al. Texture is important in improving the accuracy of mapping photovoltaic power plants:A case study of Ningxia autonomous region,China[J]. Remote Sensing, 2021, 13(19):3909. doi: 10.3390/rs13193909 url: https://www.mdpi.com/2072-4292/13/19/3909
[5]	宋业冲, 李英成, 耿中元, 等. 深度学习方法在光伏用地遥感检测中的应用[J]. 测绘科学, 2020, 45(11):84-92.
[5]	Song Y C, Li Y C, Geng Z Y, et al. Application of deep learning method in remote sensing detection of photovoltaic land[J]. Science of Surveying and Mapping, 2020, 45 (11):84-92.
[6]	Costa M V C V D, Carcalho O L F, Orlandl A G, et al. Remote sensing for monitoring photovoltaic solar plants in Brazil using deep semantic segmentation[J]. Energies, 2021, 14(10):2960. doi: 10.3390/en14102960 url: https://www.mdpi.com/1996-1073/14/10/2960
[7]	Redmon J, Divvala S, Girshick R, et al. You only look once:Unified,real-time object detection[C]// Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2016:779-788.
[8]	Redmon J, Farhadi A. YOLOv3:An incremental improvement[J/OL]. arXiv, 2018. https://arxiv.org/abs/1804.02767. url: https://arxiv.org/abs/1804.02767
[9]	Redmon J, Farhadi A. YOLO9000:Better,faster,stronger[C]// 2017 IEEE Conference on Computer Vision and Pattern Recognition. 2017:6517-6525.
[10]	龙怡灿, 雷蓉, 董杨, 等. 基于YOLOv5算法的飞机类型光学遥感识别[J]. 地球信息科学学报, 2022, 24(3):572-582. doi: 10.12082/dqxxkx.2022.210369
[10]	Long Y C, Lei R, Dong Y, et al. YOLOv5 based on aircraft type detection from remotely sensed optical images[J]. Journal of Geo-Information Science, 2022, 24 (3):572-582.
[11]	林森, 刘美怡, 陶志勇. 采用注意力机制与改进YOLOv5的水下珍品检测[J]. 农业工程学报, 2021, 37(18):307-314.
[11]	Lin S, Liu M Y, Tao Z Y. Detection of underwater treasures using attention mechanism and improved YOLOv5[J]. Transactions of the Chinese Society of Agricultural Engineering, 2021, 37 (18):307-314.
[12]	胡根生, 吴继甜, 鲍文霞, 等. 基于改进YOLOv5网络的复杂背景图像中茶尺蠖检测[J]. 农业工程学报, 2021, 37(21):191-198.
[12]	Hu G S, Wu J T, Bao W X, et al. Detection of Ectropis oblique in complex background images using improved YOLOv5[J]. Transactions of the Chinese Society of Agricultural Engineering, 2021, 37 (21):191-198.
[13]	Tan M, Pang R, Le Q V. EfficientDet:Scalable and efficient object detection[C]// IEEE Conference on Computer Vision and Pattern Recognition, 2020:10778-10787.
[14]	Han K Wang Y H, Tian Q, et al. GhostNet:More features from cheap operations[C]// Proceedings of 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition,Seattle,USA, 2020:1577-1586.
[15]	Hou Q, Zhou D, Feng J. Coordinate attention for efficient mobile network design[C]// Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition.IEEE, 2021:13713-13722.
[16]	Liu W, Anguelov D, Erhan D, et al. SSD:Single shot multibox detector[C]// European Conference on Computer Vision.Cham:Springer, 2016:21-37.
[17]	Girshick R, Donahue J, Darrell T, et al. Rich feature hierarchies for accurate object detection and semantic segmentation[C]// Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.IEEE, 2014:580-587.
[18]	Ren S, He K, 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. doi: 10.1109/TPAMI.2016.2577031 url: http://ieeexplore.ieee.org/document/7485869/
[19]	陈静, 陈静波, 孟瑜, 等. 尺度和密度约束下基于 YOLOv3 的风电塔架遥感检测方法[J]. 自然资源遥感, 2021, 33(3):54-62.doi:10.6046/zrzyyg.2020309.
[19]	Chen J, Chen J B, Meng Y, et al. Detection of wind turbine towers in remote sensing based on YOLOv3 model under scale and density constraints[J]. Remote Sensing for Natural Resources, 2021, 33(3):54-62.doi:10.6046/zrzyyg.2020309.

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