Research on Methods for Detection and Localization of Color Steel Tile Buildings

JIN Nan; WU Yongjingbang; SHI Zhongqi; YUE Qingrui; ZHENG Zexing

doi:10.3724/j.gyjzG23120810

Volume 54 Issue 2

Feb. 2024

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Article Navigation > INDUSTRIAL CONSTRUCTION > 2024 > 54(2): 58-64

JIN Nan, WU Yongjingbang, SHI Zhongqi, YUE Qingrui, ZHENG Zexing. Research on Methods for Detection and Localization of Color Steel Tile Buildings[J]. INDUSTRIAL CONSTRUCTION, 2024, 54(2): 58-64. doi: 10.3724/j.gyjzG23120810

Citation:

JIN Nan, WU Yongjingbang, SHI Zhongqi, YUE Qingrui, ZHENG Zexing. Research on Methods for Detection and Localization of Color Steel Tile Buildings[J]. INDUSTRIAL CONSTRUCTION, 2024, 54(2): 58-64. doi: 10.3724/j.gyjzG23120810

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Research on Methods for Detection and Localization of Color Steel Tile Buildings

doi: 10.3724/j.gyjzG23120810

JIN Nan^1,2,3,
WU Yongjingbang^1,2,3,
SHI Zhongqi^{1,2,3
,
,},
YUE Qingrui^1,2,3,
ZHENG Zexing^1,2,3

1. Shenzhen Urban Public Safety and Technology Institute, Shenzhen 518046, China;
2. Key Laboratory for Urban Safety Risk Monitoring and Early Warning of Emergency Management Department, Shenzhen 518038, China;
3. Safe Urban Development Institute of Science and Technology(Shenzhen), Shenzhen 518046, China

Received Date: 2023-12-08
Available Online: 2024-04-23

Abstract

Abstract

The detection and location of illegal buildings has always been a challenge for urban management, Color steel tile buildings are one of the key objects of attention in determining whether they are illegal buildings. Existing research focuses on how to detect colored steel and tile buildings, but lacks the specific street positioning of such buildings. This study aims to address this issue by proposing a framework that integrates illegal building detection with location. An empirical study in a particular street area in Shenzhen was conducted, aerial visible light images taken by drones were used as the dataset. Firstly, drones were used to collect image data from the detection area, and then DINO was applied to detect color steel tile buildings and obtain the center points of their bounding box. After coordinate system transformation, the coordinates of the color steel tile buildings were obtained, and Aruco codes were used to verify the accuracy of the location. Finally, the coordinates were correlated with street information through the interface of a map engine. The detection results indicated that DINO performed well in detecting color steel tile buildings, achieving a detection accuracy of 90%. The positioning test results indicated that when the drone was within 30 m of the measured object, the positioning accuracy could be controlled within 1 m, but the increase in the height of the drone from the measured object wouldcause the positioning accuracy to decrease. An effective framework for the detection and location of illegal buildings was proposed and validated. This method not only has high accuracy in detecting color steel tile buildings, but can also determine the specific street location of color steel tile buildings, which contributes to more accurate and efficient urban management.
- illegal building detection,
- drone positioning,
- deep learning,
- remote sensing,
- urban renewal

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References(20)

References

[1]	ZHAO J, XIAO Y, SUN S, et al. Does China's increasing coupling of'urban population'and'urban area'growth indicators reflect a growing social and economic sustainability?[J/OL]. Journal of Environmental Management, 2022, 301.[2023-12-08].https://doi.org/10.1016/j.jenvman.2021.113932.
[2]	LI B S, TAN Z C. Dynamic monitoring method of illegal buildings using spatiotemporal big data based on urban high lying zones[J]. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2020, 42:17-23.
[3]	WENJING Y. Analysis of illegally built buildings monitoring based on UAV remote sensing image[J]. Urban Geotechnical Investigation&Surveying, 2016, 6:84-87.
[4]	HE Y, MA W, MA Z, et al. Using unmanned aerial vehicle remote sensing and a monitoring information system to enhance the management of unauthorized structures[J/OL]. Applied Sciences, 2019, 9(22).[2023-12-08].https://doi.org/10.3390/app9224954.
[5]	王爽,林璐,徐健,等.无人机辅助违章建筑监测[J].测绘标准化,2017,33(1):39-41.
[6]	梁哲恒,邓鹏,姜福泉,等.基于卷积神经网络的无人机影像违章建筑检测应用[J].测绘通报,2021(4):111-115.
[7]	LIU Y, SUN Y, TAO S, et al. Discovering potential illegal construction within building roofs from UAV images using semantic segmentation and object-based change detection[J]. Photogrammetric Engineering&Remote Sensing, 2021, 87(4):263-271.
[8]	张勇.遥感与GIS在厦门市违章建筑监测与管理中的应用[J].测绘与空间地理信息,2016,39(2):75-77.
[9]	郝明,田毅,张华,等.基于DeepLab V3+深度学习的无人机影像建筑物变化检测研究[J].现代测绘,2021,44(2):1-4.
[10]	王爽,林璐,徐健,等.无人机辅助违章建筑监测[J].测绘标准化,2017,33(1):39-41.
[11]	朱建伟,袁国辉.基于倾斜摄影测量技术的无人机城市建筑监测系统在违建查找中的应用[J].工程勘察,2017,45(7):59-62.
[12]	LIU Y, YU Y, GAO R, et al. IBR-Yolov5:illegal building recognition with UAV image based on improved YOLOv5[C]//20227th International Conference on Image, Vision and Computing (ICIVC). IEEE, 2022:586-592.
[13]	ZHENG C, PENG B, CHEN B, et al. Multiscale fusion network for rural newly constructed building detection in unmanned aerial vehicle imagery[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2022, 15:9160-9173.
[14]	SHI X, HUANG H, YANG Y, et al. CSA-UNet:channel-spatial attention-based encoder-decoder network for rural blue-roofed building extraction from UAV imagery[J]. IEEE Geoscience and Remote Sensing Letters, 2022,19:1-5.
[15]	TAN Y, LI G, CAI R, et al. Mapping and modelling defect data from UAV captured images to BIM for building external wall inspection[J/OL]. Automation in Construction, 2022, 139.[2023-12-08]. https://doi.org/10.1016/j.autcon.2022.104284.
[16]	ZHANG H, LI F, LIU S, et al. Dino:Detr with improved denoising anchor boxes for end-to-end object detection[J/OL]. arXiv, 2022[2023-12-08]. https://doi.org/10.48550/arXiv.2203.03605.
[17]	DJI Enterprise. DJ-Innovations[EB/OL].(2023-12-08)[2023-12-08]. https://www.dji.com/cn/mavic-2-enterprise.
[18]	GARRIDO S,MUÑOZ S R,MADRID F J,et al. Automatic generation and detection of highly reliable fiducial markers under occlusion[J]. Pattern Recognition,2014,47(6):2280-2292.
[19]	曹宇辉,解明扬,李嘉铭,等.空地协同作业场景下无人机快速路径规划与自主降落技术[J].电光与控制,2023,30(10):1-6.
[20]	贺勇,李子豪,高正涛.基于视觉导航的无人机自主精准降落[J].电光与控制,2023,30(4):88-93.

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