Segmentation of Thermography Image of Solar Cells and Panels

Alfaro-Mejía, Estefanía; Correa, Humberto Loaiza; Franco-Mejía, Édinson; Hernández-Callejo, Luís

doi:10.1007/978-3-030-38889-8_1

Cited by 6 publications

(5 citation statements)

References 10 publications

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“…Although the methods are hardly comparable given their different structures for results (i.e., mask, box or line), their different dataset sizes and the different evaluation metrics used, a method that combined many algorithms (DIP, SVM and DL) for detecting PV modules in aIRT images and obtained an F1 score of 98.4% can be highlighted [85]. On the other hand, the worst metrics were obtained with simple DIP filters [86], which although providing fast results with small datasets required for training, are characterized by a lack in generalization. This is important for the replication of the algorithm in images acquired in different conditions and with a different quality.…”

Section: Discussionmentioning

confidence: 99%

“…The best metric among the studies was obtained with a combination of many algorithms (DIP, SVM and DL) [85]. On the other hand, the worst metrics were obtained with simple DIP filters [86]. It is important to note that not all studies are comparable, because not all have presented metrics for their performance, and they have different dataset sizes, which make the comparison difficult.…”

Section: Detection Of Pv Modulesmentioning

confidence: 99%

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Automatic Inspection of Photovoltaic Power Plants Using Aerial Infrared Thermography: A Review

2022

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In recent years, aerial infrared thermography (aIRT), as a cost-efficient inspection method, has been demonstrated to be a reliable technique for failure detection in photovoltaic (PV) systems. This method aims to quickly perform a comprehensive monitoring of PV power plants, from the commissioning phase through its entire operational lifetime. This paper provides a review of reported methods in the literature for automating different tasks of the aIRT framework for PV system inspection. The related studies were reviewed for digital image processing (DIP), classification and deep learning techniques. Most of these studies were focused on autonomous fault detection and classification of PV plants using visual, IRT and aIRT images with accuracies up to 90%. On the other hand, only a few studies explored the automation of other parts of the procedure of aIRT, such as the optimal path planning, the orthomosaicking of the acquired images and the detection of soiling over the modules. Algorithms for the detection and segmentation of PV modules achieved a maximum F1 score (harmonic mean of precision and recall) of 98.4%. The accuracy, robustness and generalization of the developed algorithms are still the main issues of these studies, especially when dealing with more classes of faults and the inspection of large-scale PV plants. Therefore, the autonomous procedure and classification task must still be explored to enhance the performance and applicability of the aIRT method.

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Section: Discussionmentioning

confidence: 99%

Section: Detection Of Pv Modulesmentioning

confidence: 99%

Automatic Inspection of Photovoltaic Power Plants Using Aerial Infrared Thermography: A Review

2022

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“…However, the presence of hot spots creates an atypical distribution of data, which leads to a loss of image contrast, making it difficult to identify the panels. Alternatively, the panels were segmented by the following methods: Template Matching [32], segmenting the image using k-means clustering [33], the active contour level sets method (MCA) and the area filtering (AF) approach [28]. These methods have been referred to as classical methods, which fail to focus only on identifying panels and do not include a method for differentiating them from the environment with panel-like structures.…”

Section: Related Workmentioning

confidence: 99%

“…For the identification of the panels, information from the thermal image has been used, together with the RGB image [24] or the 3D models [25]. However, the developments are focused on the identification of the panels using only thermal video [26,27] or thermal imaging [28].…”

Section: Introductionmentioning

confidence: 99%

Solar Panel Detection within Complex Backgrounds Using Thermal Images Acquired by UAVs

Díaz

Vlaminck

Lefkaditis³

et al. 2020

Sensors

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The installation of solar plants everywhere in the world increases year by year. Automated diagnostic methods are needed to inspect the solar plants and to identify anomalies within these photovoltaic panels. The inspection is usually carried out by unmanned aerial vehicles (UAVs) using thermal imaging sensors. The first step in the whole process is to detect the solar panels in those images. However, standard image processing techniques fail in case of low-contrast images or images with complex backgrounds. Moreover, the shades of power lines or structures similar to solar panels impede the automated detection process. In this research, two self-developed methods are compared for the detection of panels in this context, one based on classical techniques and another one based on deep learning, both with a common post-processing step. The first method is based on edge detection and classification, in contrast to the second method is based on training a region based convolutional neural networks to identify a panel. The first method corrects for the low contrast of the thermal image using several preprocessing techniques. Subsequently, edge detection, segmentation and segment classification are applied. The latter is done using a support vector machine trained with an optimized texture descriptor vector. The second method is based on deep learning trained with images that have been subjected to three different pre-processing operations. The postprocessing use the detected panels to infer the location of panels that were not detected. This step selects contours from detected panels based on the panel area and the angle of rotation. Then new panels are determined by the extrapolation of these contours. The panels in 100 random images taken from eleven UAV flights over three solar plants are labeled and used to evaluate the detection methods. The metrics for the new method based on classical techniques reaches a precision of 0.997, a recall of 0.970 and a F1 score of 0.983. The metrics for the method of deep learning reaches a precision of 0.996, a recall of 0.981 and a F1 score of 0.989. The two panel detection methods are highly effective in the presence of complex backgrounds.

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“…Limited to faults detectable through visual analysis. [41] 2020 Two segmentation techniques for photovoltaic (PV) solar panels: filtering by area and active contours level-set method (ACM LS). Refinement using morphological operations and Hough transform Solar Panel Infrared Image Datasets Dice = 0.97, IoU = 0.94 Small statistical differences between AF and ACM LS segmentation.…”

Section: Introductionmentioning

confidence: 99%

Detection and analysis of deteriorated areas in solar PV modules using unsupervised sensing algorithms and 3D augmented reality

Oulefki,

Himeur,

Trongtirakul

et al. 2024

Heliyon

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Segmentation of Thermography Image of Solar Cells and Panels

Cited by 6 publications

References 10 publications

Automatic Inspection of Photovoltaic Power Plants Using Aerial Infrared Thermography: A Review

Automatic Inspection of Photovoltaic Power Plants Using Aerial Infrared Thermography: A Review

Solar Panel Detection within Complex Backgrounds Using Thermal Images Acquired by UAVs

Detection and analysis of deteriorated areas in solar PV modules using unsupervised sensing algorithms and 3D augmented reality

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