The three year CRC-SI project 6.07 for Spatial information Business improvement applications at Ergon Energy focussed on precision guidance of aircraft over powerlines, asset and vegetation detection using LiDAR imagery, and detection and classification of tree species. The project was successfully completed in 2010 and provided the fundamental basis for the FAS project. CRC-SI project 4.3.1 (the eFAS project) will provide further developments.
Ergon Energy’s Powerline Network
Ergon Energy has over 690,000 customers spread over 1.7 million square kilometres, or 97% of the state of Queensland. Their principal asset is over 150,000km of powerline and almost 1,000,000 power poles. A significant component of their cost of supply is in the condition monitoring of the powerline assets and the vegetation in the powerline corridor. Each year Ergon spends $80 million on inspection and clearing vegetation.
Route Optimisation Techniques
In order to reduce cost it is important to conduct airborne surveillance in an efficient manner. The ARCAA team developed new route optimisation algorithms for powerline inspection aircraft based upon biologically-inspired meta-heuristic techniques were further developed and refined (calculated inspection routes involving 20% shorter flight time than human planned routes). These algorithms were characterised against alternative planning approaches.
Precision Guidance of Aircraft
Cost-effective airborne vegetation surveillance requires precision guidance of the survey aircraft in order to keep the sensors scanning the target area. New guidance algorithms for the related problem of inspecting a sequence of isolated point assets (such as power pole isolators) were developed and evaluated through simulation and flight tests.
Powerline and Vegetation Detection in LiDAR and Imagery
The aim of the automated processing is to automatically and robustly extract vegetation management clearances, thus saving on the labour costs and effort associated with manual processing of voluminous data sets. New LiDAR automatic power line detection techniques were developed, and were shown to have superior detection rates compared to commercially available LiDAR processing products (independently tested). A number of state-of-the-art local feature descriptors and machine learning classifiers were also evaluated in order to select the most appropriate features in tree species classification; a new rotation and scale invariant spectral-texture feature descriptor has also been developed and evaluated.
- Bruggemann, Troy S., Ford, Jason J., Walker, Rodney A. (2010) Control of aircraft for inspection of linear infrastructure IEEE Transactions on Control Systems Technology.
- Bruggemann, Troy S., Ford, Jason J., (2011) Compensation of Unmodeled Aircraft Dynamics in Airborne Inspection of Linear Infrastructure Assets, Australian Control Conference.
- Li, Zhengrong, Bruggemann, Troy S., Ford, Jason J., Mejias, Luis, Liu, Yuee, (2011) Towards Automated Power Line Corridor Monitoring Using Advanced Aircraft Control and Multi-source Feature Fusion, Journal of Field Robotics
- Bruggemann, Troy S., Ford, Jason J., (2011) Guidance of Aircraft for Periodic Inspection Tasks, Australian Control Conference.
- Li, Zhengrong, Hayward, Ross F., Walker, Rodney A., & Liu, Yuee (2011) A biologically inspired object spectral-texture descriptor and its application to vegetation classification in power-line corridors IEEE Geoscience and Remote Sensing Letters
- Li, Zhengrong, Hayward, Ross F., Liu, Yuee, & Walker, Rodney A. (2011) Spectral–texture feature extraction using statistical moments with application to object-based vegetation species classification International Journal of Image and Data Fusion.
- Mills, Steven, Ford, Jason J., & Mejias, Luis (2011) Vision based control for fixed wing UAVs inspecting locally linear infrastructure using skid-to-turn maneuvers Journal of Intelligent and Robotic Systems.
- Li, Zhengrong, Hayward, Ross F., Zhang, Jinglan, Jin, Hang, & Walker, Rodney A. (2010) Evaluation of spectral and texture features for object-based vegetation species classification using Support Vector Machines In International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences (Part A)
- Li, Zhengrong, Liu, Yuee, Hayward, Ross, & Walker, Rodney (2010) Empirical comparison of machine learning algorithms for image texture classification with application to vegetation management in power line corridors International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences (Part A)
- Li, Zhengrong, Liu, Yuee, Hayward, Ross F., & Walker, Rodney A. (2010) Color and texture feature fusion using kernel PCA with application to object-based vegetation species classification Proceedings of 2010 IEEE 17th International Conference on Image Processing
- Li, Zhengrong, Walker, Rodney, Hayward, Ross, & Mejias, Luis (2010) Advances in vegetation management for power line corridor monitoring using aerial remote sensing techniques Proceedings of the First International Conference on Applied Robotics for the Power Industry.
- Mills, Steven, Gerardo, Marcos, Li, Zhengrong, Cai, Jinhai, Hayward, Ross F., Mejias, Luis, & Walker, Rodney A. (2009) Evaluation of aerial remote sensing techniques for vegetation management in power line corridors IEEE Transactions on Geoscience and Remote Sensing.
- Li, Zhengrong, Hayward, Ross F., Zhang, Jinglan, Liu, Yuee, & Walker, Rodney A. (2009) Towards automatic tree crown detection and delineation in spectral feature space using PCNN and morphological reconstruction Proceedings of the 2009 IEEE International Conference on Image Processing.
- Li, Zhengrong, Liu, Yuee, Walker, Rodney A., Hayward, Ross F., & Zhang, Jinglan (2009) Towards automatic power line detection for a UAV surveillance system using pulse coupled neural filter and an improved Hough transform Machine Vision and Applications.
- Li, Zhengrong, Liu, Yuee, Hayward, Ross F., Zhang, Jinglan, & Cai, Jinhai (2008) Knowledge-based power line detection for UAV surveillance and inspection systems Proceedings of The 23rd International Conference on Image and Vision Computing New Zealand.