Deep Inspection: An Electrical Distribution Pole Parts Study VIA Deep Neural Networks

Liu, Liangchen; Zhang, Teng; Zhao, Kun; Wiliem, Arnold; Astin-Walmsley, Kieren; Lovell, Brian C.

doi:10.1109/icip.2019.8803415

Cited by 8 publications

(3 citation statements)

References 28 publications

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“…Concerning electric poles in particular, a lot of attention has gone into ways of mapping them periodically, including manned [1] and unmanned [2] aerial flights, and remote sensing pipelines [3] (we refer to [2] for a more in-depth overview). Concerning deep learning instead, deep neural networks have been used to predict possible failures [19], identifying specific poles from images [20], or finding vegetation or icing on the poles [2]. State-of-the-art methods are generally framed as an object detection problem, where the task is to find the proper bounding box surrounding a pole from an aerial image [20].…”

Section: Related Workmentioning

confidence: 99%

Reidentification of Objects From Aerial Photos With Hybrid Siamese Neural Networks

Devoto

Spinelli

Murabito

et al. 2023

IEEE Trans. Ind. Inf.

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In this article, we consider the task of reidentifying the same object in different photos taken from separate positions and angles during aerial reconnaissance, which is a crucial task for the maintenance and surveillance of critical large-scale infrastructure. To effectively hybridize deep neural networks with available domain expertise for a given scenario, we propose a customized pipeline, wherein a domain-dependent object detector is trained to extract the assets (i.e., subcomponents) present on the objects, and a siamese neural network learns to reidentify the objects, exploiting both visual features (i.e., the image crops corresponding to the assets) and the graphs describing the relations among their constituting assets. We describe a realworld application concerning the reidentification of electric poles in the Italian energy grid, showing our pipeline to significantly outperform siamese networks trained from visual information alone. We also provide a series of ablation studies of our framework to underline the effect of including topological asset information in the pipeline, learnable positional embeddings in the graphs, and the effect of different types of graph neural networks on the final accuracy.

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Section: Related Workmentioning

confidence: 99%

Reidentification of Objects From Aerial Photos With Hybrid Siamese Neural Networks

Devoto

Spinelli

Murabito

et al. 2023

IEEE Trans. Ind. Inf.

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“…Power grid assets easily suffer from corrosion and aging to different levels year by year. The mask RCNN is harnessed for performing the automatic image-based corrosion monitoring of steel transmission towers [111] and conducting the automatic inspection of the aging electrical distribution poles [112] which can significantly reduce human based inspections.…”

Section: G Image Processing Tasksmentioning

confidence: 99%

AI-powered Energy Internet Towards Carbon Neutrality: Challenges and Opportunities

Li¹

2021

Preprint

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From self-driving vehicles, voice recognition based virtual digital assistants, smart thermostats to recommendation systems, Artificial Intelligence (AI) is becoming a crucial part of the carbon neutral society that has drawn considerable interest from energy supply firms, startups, technology developers, financial institutions, national governments and the academic community. The emergence of AI initiates numerous opportunities to transform energy industry to AI-powered smart system which can revolutionize traditional approaches of creativity thinking, strategical operation, and solution seeking, especially for accelerating carbon neutrality of our society. This survey provides a comprehensive overview of fundamental principles that underpin applications of big data analysis in Energy Internet (EI), such as smart energy supply and consumption, smart health and Fintech. Next, we focus on intelligent decision-making for the energy industry and inform the state-of-the-art by thoroughly reviewing the literature. Subsequently, cybersecurity issues for AI system related to EI are discussed with recent advancements from vulnerability analysis of AI system to differential privacy and to blockchain based security technology. To our knowledge, this is one of the first academic, peer-reviewed works to provide a systematic review of AI applications for EI research and initiatives in terms of big data analysis, intelligent decision-making and AI related cybersecurity These initiatives were systematically classified into different groups according to the field of application, methodology and contribution Afterwards, potential challenges, limitations for existing research and opportunities for future directions are discussed, ranging from emerging explainable AI, to localized multi-energy marketplaces, self-driving electric vehicle charging and e-mobility. This paper can help us understand how to build smart cities and critical infrastructure for a climate-changed world towards the UN’s sustainable development goals.

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“…The goal is to detect the utility pole with SegNet, estimate the angle of detected poles with Hough Transform and predict the pole resilience with the SVM (Support Vector Machine) algorithm. Liu et al [19] proposed a method to detect utility poles from UAV images with two Faster-RCNN also initialized with the VGG16 network. First Faster R-CNN detects utility poles and the second one crop on the top half of these detected objects to detect the cap pole missing.…”

Section: Introductionmentioning

confidence: 99%

Mapping Utility Poles in Aerial Orthoimages Using ATSS Deep Learning Method

Gomes

Silva

Gonçalves

et al. 2020

Sensors

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Mapping utility poles using side-view images acquired with car-mounted cameras is a time-consuming task, mainly in larger areas due to the need for street-by-street surveying. Aerial images cover larger areas and can be feasible alternatives although the detection and mapping of the utility poles in urban environments using top-view images is challenging. Thus, we propose the use of Adaptive Training Sample Selection (ATSS) for detecting utility poles in urban areas since it is a novel method and has not yet investigated in remote sensing applications. Here, we compared ATSS with Faster Region-based Convolutional Neural Networks (Faster R-CNN) and Focal Loss for Dense Object Detection (RetinaNet ), currently used in remote sensing applications, to assess the performance of the proposed methodology. We used 99,473 patches of 256 × 256 pixels with ground sample distance (GSD) of 10 cm. The patches were divided into training, validation and test datasets in approximate proportions of 60%, 20% and 20%, respectively. As the utility pole labels are point coordinates and the object detection methods require a bounding box, we assessed the influence of the bounding box size on the ATSS method by varying the dimensions from 30×30 to 70×70 pixels. For the proposal task, our findings show that ATSS is, on average, 5% more accurate than Faster R-CNN and RetinaNet. For a bounding box size of 40×40, we achieved Average Precision with intersection over union of 50% (AP50) of 0.913 for ATSS, 0.875 for Faster R-CNN and 0.874 for RetinaNet. Regarding the influence of the bounding box size on ATSS, our results indicate that the AP50 is about 6.5% higher for 60×60 compared to 30×30. For AP75, this margin reaches 23.1% in favor of the 60×60 bounding box size. In terms of computational costs, all the methods tested remain at the same level, with an average processing time around of 0.048 s per patch. Our findings show that ATSS outperforms other methodologies and is suitable for developing operation tools that can automatically detect and map utility poles.

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Deep Inspection: An Electrical Distribution Pole Parts Study VIA Deep Neural Networks

Cited by 8 publications

References 28 publications

Reidentification of Objects From Aerial Photos With Hybrid Siamese Neural Networks

Reidentification of Objects From Aerial Photos With Hybrid Siamese Neural Networks

AI-powered Energy Internet Towards Carbon Neutrality: Challenges and Opportunities

Mapping Utility Poles in Aerial Orthoimages Using ATSS Deep Learning Method

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