Image based surface damage detection of renewable energy installations using a unified deep learning approach

Intelligent monitoring and early warning of rock mass failure is vital. To realize the early intelligent identification of dynamic fractures in the failure process of complex fractured rocks, 3D printing of the fracture network model was used to produce rock-like specimens containing 20 random joints. An algorithm for the early intelligent identification of dynamic fractures was proposed based on the YOLOv5 deep learning network model and DIC cloud. The results demonstrate an important relationship between the overall strength of the specimen with complex fractures and dynamic fracture propagation, and the overall specimen strength can be judged semi-quantitatively by counting dynamic fracture propagation. Before the initiation of each primary fracture, a strain concentration area appears, which indicates new fracture initiation. The dynamic evolution of primary fractures can be divided into four types: primary fractures, stress concentration areas, new fractures, and cross fractures. The cross fractures have the greatest impact on the overall strength of the specimen. The overall identification accuracy of the four types of fractures identified by the algorithm reached 88%, which shows that the method is fast, accurate, and effective for fracture identification and location, and classification of complex fractured rock masses.

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“…the traditional model and can effectively improve the accuracy of target recognition [28][29][30][31].…”

Section: Principle and Development Of Yolov5mentioning

confidence: 99%

“…An intelligent classification method for coal gangue has been proposed [27]. Studies have shown that the YOLOv5 algorithm model is faster and more accurate than the traditional model and can effectively improve the accuracy of target recognition [28][29][30][31].…”

Section: Introductionmentioning

confidence: 99%

A Test Method for Finding Early Dynamic Fracture of Rock: Using DIC and YOLOv5

Zhang

Chen

et al. 2022

Sensors

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“…(4) decision-making criteria The basis for sorting or clustering all schemes according to the aggregated attribute values is called decision criterion. Decision criteria can generally be divided into the following two categories: one is the optimality criterion, which can arrange all the alternatives into a complete order, and on this basis an optimal solution can always be found [15]. The other type is the satisfaction criterion, which can simplify the decision-making problem, and the cost is relatively low.…”

Section: Gray Risk Multi-attribute Decision Makingmentioning

confidence: 99%

Risk Assessment Method and Application of Renewable Energy System Based on the Background of Low-carbon Economy

2021

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Energy development has a crucial impact on social progress. Renewable energy power generation has achieved good economic and environmental benefits, and will certainly play an important role in the future energy structure. The purpose of this paper is to study risk assessment methods for renewable energy systems and their applications in the context of a low-carbon economy. The grey relational clustering analysis method is used to attempt to regionally cluster the vulnerability of renewable energy in each province. By introducing risk preference, this paper discusses the impact of different decision makers' risk awareness on the assessment results. The classification results show that the renewable energy vulnerability indicators selected in this paper have good discrimination and representativeness, and can be used as the indicator system of the vulnerability comprehensive decision-making assessment model for decision-making analysis and regional vulnerability comparative analysis.

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“…The commonly used methods are: vibration detection [6], acoustic emission technology [7], ultrasonic flaw detection [8], strain detection [9], infrared thermal imaging [10], etc. With the maturity of UAV technology equipped with highdefinition cameras and the wide application of deep learning technology in the field of object detection [11]. A deep learning-based defect detection method for wind turbine blades emerges as the times require.…”

Section: Introductionmentioning

confidence: 99%

An Improved Algorithm for Wind Turbine Blade Defect Detection

et al. 2022

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With the increase in wind power generation, wind turbine blades require regular inspections to ensure they continue to operate safely. You only look once (YOLO) is one of the most widely used object detection algorithms and is easy to deploy into drone devices. To enhance the real-time detection of small target defects in wind turbine blades, this paper proposes an improved attention and feature balanced YOLO (AFB-YOLO) algorithm based on YOLOv5s. Specifically, AFB-YOLO improves the feature pyramid network by using weighted feature fusion and cross-scale connections. The improved feature pyramid network solves the problem that most previous feature pyramid networks treat all input features equally, and obtains more feature information. Furthermore, the coordinate attention (CA) module is introduced into the network to augment the representations of the objects of interest. Finally, the paper redesigned the loss function through efficient intersection over union (EIoU) loss to make the model obtain a better localization effect. The experimental results on the imagery of wind turbine blade defects indicate that our method shows significant gains in performance. The mean average precision (mAP50) of AFB-YOLO is 83.7% and the detection accuracy is improved by 4.0% compared to the original YOLOv5s model. The experiments in this paper demonstrate that AFB-YOLO is more effective and robust than state-of-theart detectors.INDEX TERMS wind turbine blade, YOLOv5s, feature fusion, coordinate attention, EIoU loss.

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Image based surface damage detection of renewable energy installations using a unified deep learning approach

Cited by 44 publications

References 28 publications

A Test Method for Finding Early Dynamic Fracture of Rock: Using DIC and YOLOv5

A Test Method for Finding Early Dynamic Fracture of Rock: Using DIC and YOLOv5

Risk Assessment Method and Application of Renewable Energy System Based on the Background of Low-carbon Economy

An Improved Algorithm for Wind Turbine Blade Defect Detection

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