22.2.5 Power Line Extraction, Powerline Extraction, Radar, SAR, Lidar, Laser, Depth

Chapter Contents (Back)
Power Line Detection. Aerial Image Analysis. SAR. Radar.

Jones, D.I.,
Aerial inspection of overhead power lines using video: estimation of image blurring due to vehicle and camera motion,
VISP(147), No. 2, April 2000, pp. 157. 0005
BibRef

Golightly, I.[Ian], Jones, D.[Dewi],
Corner detection and matching for visual tracking during power line inspection,
IVC(21), No. 9, September 2003, pp. 827-840.
Elsevier DOI 0308
BibRef

Sun, C.M.[Chang-Ming], Jones, R.[Ronald], Talbot, H.[Hugues], Wu, X.L.[Xiao-Liang], Cheong, K.[Kevin], Beare, R.[Richard], Buckley, M.[Michael], Berman, M.[Mark],
Measuring the distance of vegetation from powerlines using stereo vision,
PandRS(60), No. 4, June 2006, pp. 269-283.
Elsevier DOI
PDF File. 0610
Stereo matching; powerline inspection; power pole segmentation; vegetation clearance; 3D vegetation surface BibRef

Li, Z.R.[Zheng-Rong], Liu, Y.[Yuee], Walker, R.[Rodney], Hayward, R.[Ross], Zhang, J.L.[Jing-Lan],
Towards automatic power line detection for a UAV surveillance system using pulse coupled neural filter and an improved Hough transform,
MVA(21), No. 5, August 2010, pp. 677-686.
WWW Link. 1011
BibRef

Liu, Y.[Yuee], Li, Z.R.[Zheng-Rong], Hayward, R.[Ross], Walker, R.[Rodney], Jin, H.[Hang],
Classification of Airborne LIDAR Intensity Data Using Statistical Analysis and Hough Transform with Application to Power Line Corridors,
DICTA09(462-467).
IEEE DOI 0912
BibRef

Li, Z.R.[Zheng-Rong], Liu, Y.[Yuee], Hayward, R.[Ross], Zhang, J.L.[Jing-Lan], Cai, J.H.[Jin-Hai],
Knowledge-based power line detection for UAV surveillance and inspection systems,
IVCNZ08(1-6).
IEEE DOI 0811
BibRef

Mills, S.J., Gerardo Castro, M.P., Li, Z., Cai, J., Hayward, R., Mejias, L., Walker, R.A.,
Evaluation of Aerial Remote Sensing Techniques for Vegetation Management in Power-Line Corridors,
GeoRS(48), No. 9, September 2010, pp. 3379-3390.
IEEE DOI 1008
BibRef

Li, Z.R.[Zheng-Rong], Hayward, R., Zhang, J.L.[Jing-Lan], Liu, Y.[Yuee],
Individual Tree Crown Delineation Techniques for Vegetation Management in Power Line Corridor,
DICTA08(148-154).
IEEE DOI 0812
BibRef

Ma, Q., Goshi, D.S., Shih, Y.C., Sun, M.T.,
An Algorithm for Power Line Detection and Warning Based on a Millimeter-Wave Radar Video,
IP(20), No. 12, December 2011, pp. 3534-3543.
IEEE DOI 1112
BibRef

Zhu, L.L.[Ling-Li], Hyyppä, J.[Juha],
Fully-Automated Power Line Extraction from Airborne Laser Scanning Point Clouds in Forest Areas,
RS(6), No. 11, 2014, pp. 11267-11282.
DOI Link 1412

See also Use of Airborne and Mobile Laser Scanning for Modeling Railway Environments in 3D, The. BibRef

Cheng, L.[Liang], Tong, L.H.[Li-Hua], Wang, Y.[Yu], Li, M.C.[Man-Chun],
Extraction of Urban Power Lines from Vehicle-Borne LiDAR Data,
RS(6), No. 4, 2014, pp. 3302-3320.
DOI Link 1405

See also Building Point Detection from Vehicle-Borne LiDAR Data Based on Voxel Group and Horizontal Hollow Analysis. BibRef

Guo, B.[Bo], Huang, X.F.[Xian-Feng], Zhang, F.[Fan], Sohn, G.[Gunho],
Classification of airborne laser scanning data using JointBoost,
PandRS(100), No. 1, 2015, pp. 71-83.
Elsevier DOI 1502
LiDAR BibRef

Sohn, G.[Gunho], Jwa, Y.[Yoonseok], Kim, H.B.[Heungsik Brian],
Automatic Powerline Scene Classification And Reconstruction Using Airborne Lidar Data,
AnnalsPRS(I-3), No. 2012, pp. 167-172.
HTML Version. 1209
BibRef
Earlier: A2, A1, A3:
Automatic 3D Powerline Reconstruction Using Airborne LIDAR Data,
Laser09(105). 0909

See also Hybrid Ensemble Classification of Tree Genera Using Airborne LiDAR Data. BibRef

Luo, C., Sohn, G.,
Line-based Classification of Terrestrial Laser Scanning Data using Conditional Random Field,
SSG13(155-160).
DOI Link 1402
BibRef

Kim, H.B.[Heungsik Brian], Sohn, G.[Gunho],
Random Forests Based Multiple Classifier System For Power-Line Scene Classification,
Laser11(xx-yy).
DOI Link 1109
BibRef
Earlier:
3D classification of power-line scene from airborne laser scanning data using Random Forests,
PCVIA10(A:126).
PDF File. 1009
BibRef

Jwa, Y.[Yoonseok], Sohn, G.[Gunho],
A multi-level span analysis for improving 3D power-line reconstruction performance using airborne laser scanning data,
PCVIA10(A:97).
PDF File. 1009
BibRef

Cho, C.J., Ko, H.,
Video-Based Dynamic Stagger Measurement of Railway Overhead Power Lines Using Rotation-Invariant Feature Matching,
ITS(16), No. 3, June 2015, pp. 1294-1304.
IEEE DOI 1506
Feature extraction BibRef

Conde, B.[Borja], Villarino, A.[Alberto], Cabaleiro, M.[Manuel], Gonzalez-Aguilera, D.[Diego],
Geometrical Issues on the Structural Analysis of Transmission Electricity Towers Thanks to Laser Scanning Technology and Finite Element Method,
RS(7), No. 9, 2015, pp. 11551.
DOI Link 1511
BibRef

Chen, Y.P.[Yun-Ping], Li, Y.[Yang], Zhang, H.X.[Hui-Xiong], Tong, L.[Ling], Cao, Y.X.[Yong-Xing], Xue, Z.[Zhihang],
Automatic power line extraction from high resolution remote sensing imagery based on an improved Radon transform,
PR(49), No. 1, 2016, pp. 174-186.
Elsevier DOI 1511
High resolution BibRef

Li, Q.Q.[Qing-Quan], Chen, Z.P.[Zhi-Peng], Hu, Q.[Qingwu],
A Model-Driven Approach for 3D Modeling of Pylon from Airborne LiDAR Data,
RS(7), No. 9, 2015, pp. 11501.
DOI Link 1511
BibRef

Guo, B.[Bo], Li, Q.Q.[Qing-Quan], Huang, X.F.[Xian-Feng], Wang, C.S.[Chi-Sheng],
An Improved Method for Power-Line Reconstruction from Point Cloud Data,
RS(8), No. 1, 2016, pp. 36.
DOI Link 1602
BibRef

Guo, B.[Bo], Huang, X.F.[Xian-Feng], Li, Q.Q.[Qing-Quan], Zhang, F.[Fan], Zhu, J.S.[Jia-Song], Wang, C.S.[Chi-Sheng],
A Stochastic Geometry Method for Pylon Reconstruction from Airborne LiDAR Data,
RS(8), No. 3, 2016, pp. 243.
DOI Link 1604
BibRef

Matikainen, L.[Leena], Lehtomäki, M.[Matti], Ahokas, E.[Eero], Hyyppä, J.[Juha], Karjalainen, M.[Mika], Jaakkola, A.[Anttoni], Kukko, A.[Antero], Heinonen, T.[Tero],
Remote sensing methods for power line corridor surveys,
PandRS(119), No. 1, 2016, pp. 10-31.
Elsevier DOI 1610
Power line BibRef

Pastucha, E.[Elzbieta],
Catenary System Detection, Localization and Classification Using Mobile Scanning Data,
RS(8), No. 10, 2016, pp. 801.
DOI Link 1609
overhead power lines. BibRef

Jung, J.W.[Jae-Wook], Chen, L.[Leihan], Sohn, G.[Gunho], Luo, C.[Chao], Won, J.U.[Jong-Un],
Multi-Range Conditional Random Field for Classifying Railway Electrification System Objects Using Mobile Laser Scanning Data,
RS(8), No. 12, 2016, pp. 1008.
DOI Link 1612
BibRef

Zhang, Y.[Yong], Yuan, X.X.[Xiu-Xiao], Fang, Y.[Yi], Chen, S.Y.[Shi-Yu],
UAV Low Altitude Photogrammetry for Power Line Inspection,
IJGI(6), No. 1, 2017, pp. xx-yy.
DOI Link 1702
BibRef

Zhang, Y.[Yong], Yuan, X.X.[Xiu-Xiao], Li, W.Z.[Wen-Zhuo], Chen, S.Y.[Shi-Yu],
Automatic Power Line Inspection Using UAV Images,
RS(9), No. 8, 2017, pp. xx-yy.
DOI Link 1708
BibRef

Jaw, Y.[Yoonseok], Sohn, G.[Gunho],
Wind adaptive modeling of transmission lines using minimum description length,
PandRS(125), No. 1, 2017, pp. 193-206.
Elsevier DOI 1703
3D transmission line model BibRef

Cerón, A.[Alexander], Mondragón, I.[Iván], Prieto, F.[Flavio],
Real-time transmission tower detection from video based on a feature descriptor,
IET-CV(11), No. 1, February 2017, pp. 33-42.
DOI Link 1703
BibRef

Jiang, S.[San], Jiang, W.[Wanshou], Huang, W.[Wei], Yang, L.[Liang],
UAV-Based Oblique Photogrammetry for Outdoor Data Acquisition and Offsite Visual Inspection of Transmission Line,
RS(9), No. 3, 2017, pp. xx-yy.
DOI Link 1704
BibRef

Jiang, S.[San], Jiang, W.[Wanshou],
On-Board GNSS/IMU Assisted Feature Extraction and Matching for Oblique UAV Images,
RS(9), No. 8, 2017, pp. xx-yy.
DOI Link 1708
BibRef

Qin, X.Y.[Xin-Yan], Wu, G.P.[Gong-Ping], Ye, X.[Xuhui], Huang, L.[Le], Lei, J.[Jin],
A Novel Method to Reconstruct Overhead High-Voltage Power Lines Using Cable Inspection Robot LiDAR Data,
RS(9), No. 7, 2017, pp. xx-yy.
DOI Link 1708
BibRef

Wang, Y.J.[Yan-Jun], Chen, Q.[Qi], Liu, L.[Lin], Zheng, D.Y.[Dun-Yong], Li, C.[Chaokui], Li, K.[Kai],
Supervised Classification of Power Lines from Airborne LiDAR Data in Urban Areas,
RS(9), No. 8, 2017, pp. xx-yy.
DOI Link 1708
BibRef

Zhou, R.[Ruqin], Jiang, W.[Wanshou], Huang, W.[Wei], Xu, B.[Bo], Jiang, S.[San],
A Heuristic Method for Power Pylon Reconstruction from Airborne LiDAR Data,
RS(9), No. 11, 2017, pp. xx-yy.
DOI Link 1712
BibRef

Chen, C.[Chi], Yang, B.S.[Bi-Sheng], Song, S.[Shuang], Peng, X.Y.[Xiang-Yang], Huang, R.G.[Rong-Gang],
Automatic Clearance Anomaly Detection for Transmission Line Corridors Utilizing UAV-Borne LIDAR Data,
RS(10), No. 4, 2018, pp. xx-yy.
DOI Link 1805
BibRef

Yang, B.S.[Bi-Sheng], Chen, C.[Chi],
Automatic registration of UAV-borne sequent images and LiDAR data,
PandRS(101), No. 1, 2015, pp. 262-274.
Elsevier DOI 1503
Unmanned aerial vehicles mapping BibRef

Wang, Y.J.[Yan-Jun], Chen, Q.[Qi], Liu, L.[Lin], Li, X.[Xiong], Sangaiah, A.K.[Arun Kumar], Li, K.[Kai],
Systematic Comparison of Power Line Classification Methods from ALS and MLS Point Cloud Data,
RS(10), No. 8, 2018, pp. xx-yy.
DOI Link 1809
BibRef

Shi, Z.W.[Zhen-Wei], Kang, Z.Z.[Zhi-Zhong], Lin, Y.[Yi], Liu, Y.[Yu], Chen, W.[Wei],
Automatic Recognition of Pole-Like Objects from Mobile Laser Scanning Point Clouds,
RS(10), No. 12, 2018, pp. xx-yy.
DOI Link 1901
BibRef

Zhou, R.Q.[Ru-Qin], Jiang, W.S.[Wan-Shou], Jiang, S.[San],
A Novel Method for High-Voltage Bundle Conductor Reconstruction from Airborne LiDAR Data,
RS(10), No. 12, 2018, pp. xx-yy.
DOI Link 1901
BibRef

Ortega, S.[Sebastián], Trujillo, A.[Agustín], Santana, J.M.[José Miguel], Suárez, J.P.[José Pablo], Santana, J.[Jaisiel],
Characterization and modeling of power line corridor elements from LiDAR point clouds,
PandRS(152), 2019, pp. 24-33.
Elsevier DOI 1905
Power line, Classification, Point cloud, Modeling BibRef

Zhang, H.[Heng], Yang, W.[Wen], Yu, H.[Huai], Zhang, H.[Haijian], Xia, G.S.[Gui-Song],
Detecting Power Lines in UAV Images with Convolutional Features and Structured Constraints,
RS(11), No. 11, 2019, pp. xx-yy.
DOI Link 1906
BibRef

Chen, S.C.[Shi-Chao], Wang, C.[Cheng], Dai, H.[Huayang], Zhang, H.B.[He-Bing], Pan, F.F.[Fei-Fei], Xi, X.H.[Xiao-Huan], Yan, Y.G.[Yue-Guan], Wang, P.[Pu], Yang, X.B.[Xue-Bo], Zhu, X.X.[Xiao-Xiao], Aben, A.[Ardana],
Power Pylon Reconstruction Based on Abstract Template Structures Using Airborne LiDAR Data,
RS(11), No. 13, 2019, pp. xx-yy.
DOI Link 1907
BibRef

Awrangjeb, M.[Mohammad],
Extraction of Power Line Pylons and Wires Using Airborne LiDAR Data at Different Height Levels,
RS(11), No. 15, 2019, pp. xx-yy.
DOI Link 1908
BibRef

Peng, S.[Shuwen], Xi, X.H.[Xiao-Huan], Wang, C.[Cheng], Dong, P.L.[Pin-Liang], Wang, P.[Pu], Nie, S.[Sheng],
Systematic Comparison of Power Corridor Classification Methods from ALS Point Clouds,
RS(11), No. 17, 2019, pp. xx-yy.
DOI Link 1909
BibRef

Sánchez-Rodríguez, A.[Ana], Soilán, M.[Mario], Cabaleiro, M.[Manuel], Arias, P.[Pedro],
Automated Inspection of Railway Tunnels' Power Line Using LiDAR Point Clouds,
RS(11), No. 21, 2019, pp. xx-yy.
DOI Link 1911
BibRef

Tomaszewski, M.[Michal], Michalski, P.[Pawel], Ruszczak, B.[Bogdan], Zator, S.[Slawomir],
Detection of power line insulators on digital images with the use of laser spots,
IET-IPR(13), No. 12, October 2019, pp. 2358-2366.
DOI Link 1911
BibRef

Zhang, R.Z.[Rui-Zhuo], Yang, B.S.[Bi-Sheng], Xiao, W.[Wen], Liang, F.[Fuxun], Liu, Y.[Yang], Wang, Z.M.[Zi-Ming],
Automatic Extraction of High-Voltage Power Transmission Objects from UAV Lidar Point Clouds,
RS(11), No. 22, 2019, pp. xx-yy.
DOI Link 1911
BibRef

Tao, X.[Xian], Zhang, D.P.[Da-Peng], Wang, Z.H.[Zi-Hao], Liu, X.L.[Xi-Long], Zhang, H.Y.[Hong-Yan], Xu, D.[De],
Detection of Power Line Insulator Defects Using Aerial Images Analyzed With Convolutional Neural Networks,
SMCS(50), No. 4, April 2020, pp. 1486-1498.
IEEE DOI 2004
Insulators, Inspection, Feature extraction, Object detection, Shape, Support vector machines, Convolutional neural networks, insulators BibRef

Jung, J.[Jaehoon], Che, E.[Erzhuo], Olsen, M.J.[Michael J.], Shafer, K.C.[Katherine C.],
Automated and efficient powerline extraction from laser scanning data using a voxel-based subsampling with hierarchical approach,
PandRS(163), 2020, pp. 343-361.
Elsevier DOI 2005
Powerlines, Lidar, Laser scanning, Point cloud, Voxel-based subsampling BibRef

Qiao, S.J.[Si-Jia], Sun, Y.[Yu], Zhang, H.P.[Hao-Peng],
Deep Learning Based Electric Pylon Detection in Remote Sensing Images,
RS(12), No. 11, 2020, pp. xx-yy.
DOI Link 2006
BibRef

Munir, N.[Nosheen], Awrangjeb, M.[Mohammad], Stantic, B.[Bela],
Automatic Extraction of High-Voltage Bundle Subconductors Using Airborne LiDAR Data,
RS(12), No. 18, 2020, pp. xx-yy.
DOI Link 2009
BibRef

Knyaz, V.A.[Vladimir A.], Kniaz, V.V.[Vladimir V.], Remondino, F.[Fabio], Zheltov, S.Y.[Sergey Y.], Gruen, A.[Armin],
3D Reconstruction of a Complex Grid Structure Combining UAS Images and Deep Learning,
RS(12), No. 19, 2020, pp. xx-yy.
DOI Link 2010
radio or television towers. BibRef

Kniaz, V.V.[Vladimir V.], Zheltov, S.Y.[Sergey Y.], Remondino, F.[Fabio], Knyaz, V.A.[Vladimir A.], Bordodymov, A., Gruen, A.[Armin],
Wire Structure Image-based 3d Reconstruction Aided By Deep Learning,
ISPRS20(B2:435-441).
DOI Link 2012
BibRef

Pastucha, E.[Elzbieta], Puniach, E.[Edyta], Scislowicz, A.[Agnieszka], Cwiakala, P.[Pawel], Niewiem, W.[Witold], Wiacek, P.[Pawel],
3D Reconstruction of Power Lines Using UAV Images to Monitor Corridor Clearance,
RS(12), No. 22, 2020, pp. xx-yy.
DOI Link 2011
BibRef

Nardinocchi, C., Balsi, M., Esposito, S.,
Fully Automatic Point Cloud Analysis for Powerline Corridor Mapping,
GeoRS(58), No. 12, December 2020, pp. 8637-8648.
IEEE DOI 2012
Wires, Poles and towers, Laser radar, Task analysis, Inspection, Vegetation mapping, Data analysis, unmanned aerial vehicles (UAVs) BibRef

Deidda, M., Pala, A., Sanna, G.,
Modelling A Cell Tower Using SFM: Automated Detection of Structural Elements From Skeleton Extraction on A Point Cloud,
ISPRS20(B2:399-406).
DOI Link 2012
BibRef

Yu, W., Xi, J., Wu, Z., Lei, W., Zhu, C., Tang, T.,
A Method for Extracting Substation Equipment Based on UAV Laser Scanning Point Clouds,
SmartCityApp20(413-419).
DOI Link 2012
BibRef

Kähler, O., Hochstöger, S., Kemper, G., Birchbauer, J.,
Automating Powerline Inspection: A Novel Multisensor System for Data Analysis Using Deep Learning,
ISPRS20(B4:747-754).
DOI Link 2012
BibRef

Wu, Z., Lei, W., Sun, W., Chen, C., Wang, H., Yu, W., Zhong, H., Zhu, C., Xi, J., Yang, B.,
Power Transmission Line Reconstruction From Sequential Oblique UAV Images,
ISPRS20(B2:515-520).
DOI Link 2012
BibRef

Wu, Z., Wang, H., Yu, W., Xi, J., Lei, W., Tang, T.,
3d High-efficiency and High-precision Model-driven Modelling for Power Transmission Tower,
SmartCityApp20(421-426).
DOI Link 2012
BibRef

Choi, H., Koo, G., Kim, B.J., Kim, S.W.[S. Woo],
Real-time Power Line Detection Network using Visible Light and Infrared Images,
IVCNZ19(1-6)
IEEE DOI 2004
convolutional neural nets, image fusion, image segmentation, infrared imaging, inspection, learning (artificial intelligence), Real-time BibRef

Jiang, S., Jiang, W.,
UAV-based Oblique Photogrammetry for 3d Reconstruction of Transmission Line: Practices and Applications,
UAV-g19(401-406).
DOI Link 1912
BibRef

Toschi, I., Morabito, D., Grilli, E., Remondino, F., Carlevaro, C., Cappellotto, A., Tamagni, G., Maffeis, M.,
Cloud-based Solution for Nationwide Power Line Mapping,
Semantics3D19(119-126).
DOI Link 1912
BibRef

Pu, S., Xie, L., Ji, M., Zhao, Y., Liu, W., Wang, L., Zhao, Y., Yang, F., Qiu, D.,
Real-time Powerline Corridor Inspection By Edge Computing of UAV Lidar Data,
UAV-g19(547-551).
DOI Link 1912
BibRef

Yermo, M., Martínez, J., Lorenzo, O.G., Vilariño, D.L., Cabaleiro, J.C., Pena, T.F., Rivera, F.F.,
Automatic Detection and Characterisation of Power Lines and Their Surroundings Using Lidar Data,
Laser19(1161-1168).
DOI Link 1912
BibRef

Liu, L., Zhang, T., Zhao, K., Wiliem, A., Astin-Walmsley, K., Lovell, B.,
Deep Inspection: An Electrical Distribution Pole Parts Study VIA Deep Neural Networks,
ICIP19(4170-4174)
IEEE DOI 1910
electrical distribution pole, integrated inspection system, deep neural networks, object detection, imbalanced data classification BibRef

Ganovelli, F., Malomo, L., Scopigno, R.,
Reconstructing Power Lines from Images,
IVCNZ18(1-6)
IEEE DOI 1902
Wires, Cameras, Image segmentation, Image reconstruction, Image edge detection, power lines BibRef

McCulloch, J.[Josh], Green, R.[Richard],
Conductor Reconstruction for Dynamic Line Rating Using Vehicle-Mounted LiDAR,
RS(12), No. 22, 2020, pp. xx-yy.
DOI Link 2011
BibRef
Earlier:
Utility pole extraction using vehicle-mounted LIDAR for dynamic line rating,
IVCNZ17(1-5)
IEEE DOI 1902
BibRef
And:
Density Based Recovery of Urban Power Lines Using Vehicle-Mounted LiDAR,
IVCNZ18(1-5)
IEEE DOI 1902
BibRef
Earlier:
Extraction of utility poles in LIDAR scans using cross-sectional slices,
ICVNZ16(1-4)
IEEE DOI 1701
Conductors, Meters, Laser radar, Clustering algorithms, Poles and towers, Urban areas, lidar. optical radar, principal component analysis, radar clutter, road vehicle radar, utility pole extraction, dynamic line rating. Clustering algorithms BibRef

Pontecorvo, C.[Carmine], Redding, N.J.[Nicholas J.],
Non-Periodic Translation Symmetry Detection Using Global Self Similarity,
DICTA17(1-8)
IEEE DOI 1804
Multiple poles and shadows in aerial images. correlation methods, image matching, object detection, actual aerial imagery, approximate size, blobs, consistent regions, Tensile stress BibRef

Awrangjeb, M., Jonas, D., Zhou, J.,
An Automatic Technique for Power Line Pylon Detection from Point Cloud Data,
DICTA17(1-8)
IEEE DOI 1804
feature extraction, object detection, trees (mathematics), candidate pylons, connected component analysis, Wires BibRef

Maeda, K., Takahashi, S., Ogawa, T., Haseyama, M.,
Automatic estimation of deterioration level on transmission towers via deep extreme learning machine based on local receptive field,
ICIP17(2379-2383)
IEEE DOI 1803
Estimation, Feature extraction, Inspection, Machine learning, Poles and towers, Training, Visualization, transmission tower BibRef

Cabello, F.C.[Frank C.], Iano, Y.[Yuzo], Arthur, R.[Rangel], Dueñas, A.[Abel], León, J.[Julio], Caetano, D.G.[Diogo G.],
Automatic Detection of Utility Poles Using the Bag of Visual Words Method for Different Feature Extractors,
CAIP17(II: 116-126).
Springer DOI 1708
BibRef

Gubbi, J.[Jayavardhana], Varghese, A.[Ashley], Balamuralidhar, P.,
A new deep learning architecture for detection of long linear infrastructure,
MVA17(207-210)
DOI Link 1708
Adaptation models, Data models, Drones, Feature extraction, Machine learning, Monitoring, Training BibRef

Zhou, X.[Xiao], Zheng, X.L.[Xiao-Liang], Ou, K.[Kejun],
Power line detect system based on stereo vision and FPGA,
ICIVC17(715-719)
IEEE DOI 1708
Decision making, Digital signal processing, Field programmable gate arrays, Image edge detection, Real-time systems, Robustness, Shape, FPGA, census transform, morphology, power line detect, real time, stereo, vision BibRef