Electrical distribution poles are important assets in electricity supply. These poles need to be maintained in good condition to ensure they protect community safety, maintain reliability of supply, and meet legislative obligations. However, maintaining such a large volumes of assets is an expensive and challenging task. To address this, recent approaches utilise imagery data captured from helicopter and or drone inspections. Whilst reducing the cost for manual inspection, manual analysis on each image is still required. As such, several image-based automated inspection systems have been proposed. In this paper, we target two major challenges: tiny object detection and extremely imbalanced datasets, which currently hinder the wide deployment of the automatic inspection. We propose a novel two-stage zoom-in detection method to gradually focus on the object of interest. To address the imbalanced dataset problem, we propose the resampling as well as reweighting schemes to iteratively adapt the model to the large intra-class variation of major class and balance the contributions to the loss from each class. Finally, we integrate these components together and devise a novel automatic inspection framework. Extensive experiments demonstrate that our proposed approaches are effective and can boost the performance compared to the baseline methods.
Many distribution pole inspections are still performed by the inspection team travelling on foot @cite_17 . They use varied methods such as visible spectrum cameras, infrared cameras, binoculars, and corona detection camera to perform the visual inspection on the pole and attached components such as conductors, transformers and insulators. This type of inspection is labour intensive and time consuming. Extreme weather and difficult terrains can also present challenges.
@cite_17
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The inspection of high voltage power transmission lines is mainly carried out by manned aerial vehicles or foot patrol. However, these maintenance methodologies for inspection are somehow inefficient and expensive. Moreover, helicopter assisted inspection endangers the human life. Recently, unmanned aerial vehicles have been under development in several research centers all over the world due to its potential applications. In this paper, we present an unmanned aerial system based on the quadrotor helicopter for high voltage power line inspection. Our interest is to equip the quadrotor helicopter with the necessary payload in order to be able to carry out a qualitative inspection, therefore the hardware architecture of the aerial robotic system is presented. Finally, some experimental results are shown to demonstrate the feasibility of the proposed system. The purpose of this paper is to present the most important achievements in the field of distribution power line inspection by mobile robots. Stimulated by the need for fast, accurate, safe and low-cost power line inspection, which would increase the quality of power delivery, the field of automated power line inspection has witnessed rapid development over the last decade. This paper addresses automated helicopter inspection, inspection with flying robots and inspection with climbing robots. The first attempts to automate power line inspection were conducted in the field of helicopter inspection. In recent years, however, the research was mostly focused on flying and climbing robots. These two types of robots for automated power line inspection are critically assessed according to four important characteristics: design requirements, inspection quality, autonomy and universality of inspection. Besides, some general not yet identified problems and tasks of inspection robots, which should be addressed in the future, are presented. In conclusion, the two robot types have specific benefits and drawbacks so that none can currently be considered generally advantageous. Advances in autonomous inspection of subsea facilities using a 3 Dimensional(3D) LIght Detection And Ranging (LiDAR) sensor are examined to illustrate the favorable enhancement of safety, reliability, reduction in risks, economic benefits and superior data products compared to conventional means. These benefits provide operators with significant improvements over general visual inspection by the addition of sensors that produce 3D models of the structure being inspected. Examples are provided illustrating test data from operations conducted from 2012-2013. Supported by funding from the Research Partnership to Secure Energy for America (RPSEA) Lockheed Martin MST teamed with the innovative company 3D at Depth to incorporate their new DP2TM 3D LiDAR onto the proven Marlin® Autonomous Underwater Vehicle. The objectives of the project include: Survey using combination of high resolution 3D Sonar and 3D LiDAR Generation of high fidelity 3D models Detection and localization of structural changes vs. reference model Lockheed Martin has successfully integrated the laser into the Marlin shore based laboratory addressing the challenges associated with coordinating a scanning laser from a moving platform and producing high resolution georegistered images. Testing will be conducted in early 2014 to validate the system in the Atlantic Ocean waters offshore Palm Beach, Florida. Three dimensional georegistered models of an entire scene can be rapidly collected providing a clear vision of the underwater scene along high resolution 3D models of the imaged item. Data collected at sea and in the laboratory will be presented demonstrating the performance of the system. Application to deepwater life of field inspection will be presented with evidence gained from offshore trials. This emergent technology supports Subsea Facility Inspection Repair and Maintenance, Integrity Management Inspections of Marine Risers, Moorings and anchors, Subsea Pipelines, Flowlines, Umbilicals, and supporting subsea infrastructure. In order to effectively detect faults and maintain heavy machines, a standard practice in several organizations is to conduct regular manual inspections. The procedure for conducting such inspections requires marking of the damaged components on a standardized inspection sheet which is then camera scanned. These sheets are marked for different faults in corresponding machine zones using hand-drawn arrows and text. As a result, the reading environment is highly unstructured and requires a domain expert while extracting the manually marked information. In this paper, we propose a novel pipeline to build an information extraction system for such machine inspection sheets, utilizing state-of-the-art deep learning and computer vision techniques. The pipeline proceeds in the following stages: (1) localization of different zones of the machine, arrows and text using a combination of template matching, deep learning and connected components, and (2) mapping the machine zone to the corresponding arrow head and the text segment to the arrow tail, followed by pairing them to get the correct damage code for each zone. Experiments were performed on a dataset collected from an anonymous real world manufacturing unit. Results demonstrate the efficacy of the proposed approach and we also report the accuracy for each step in the pipeline.