On the detection of solar panels by image processing techniques

Salamanca, Santiago; Merchán, Pilar; García, I.

doi:10.1109/med.2017.7984163

Cited by 15 publications

(8 citation statements)

References 8 publications

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“…For example, this is the case of vehicle localization [ 6 ], traffic sign detection [ 7 ] or car plates [ 8 ]. Another related problem which resembles the considered one is the detection of solar panel structures (and their orientations) in images of photovoltaic plants with no lighting restrictions, and using texture features combined with image processing techniques [ 9 ]. Some other related applications to be considered here are text and objects detection inside segmented billboard images [ 10 ] or the localization of billboards on streamed sport videos [ 11 ].…”

Section: Introductionmentioning

confidence: 99%

SSD vs. YOLO for Detection of Outdoor Urban Advertising Panels under Multiple Variabilities

Morera

Sánchez

Moreno

et al. 2020

Sensors

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This work compares Single Shot MultiBox Detector (SSD) and You Only Look Once (YOLO) deep neural networks for the outdoor advertisement panel detection problem by handling multiple and combined variabilities in the scenes. Publicity panel detection in images offers important advantages both in the real world as well as in the virtual one. For example, applications like Google Street View can be used for Internet publicity and when detecting these ads panels in images, it could be possible to replace the publicity appearing inside the panels by another from a funding company. In our experiments, both SSD and YOLO detectors have produced acceptable results under variable sizes of panels, illumination conditions, viewing perspectives, partial occlusion of panels, complex background and multiple panels in scenes. Due to the difficulty of finding annotated images for the considered problem, we created our own dataset for conducting the experiments. The major strength of the SSD model was the almost elimination of False Positive (FP) cases, situation that is preferable when the publicity contained inside the panel is analyzed after detecting them. On the other side, YOLO produced better panel localization results detecting a higher number of True Positive (TP) panels with a higher accuracy. Finally, a comparison of the two analyzed object detection models with different types of semantic segmentation networks and using the same evaluation metrics is also included.

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Section: Introductionmentioning

confidence: 99%

SSD vs. YOLO for Detection of Outdoor Urban Advertising Panels under Multiple Variabilities

Morera

Sánchez

Moreno

et al. 2020

Sensors

View full text Add to dashboard Cite

show abstract

“…For aerial images, Tribak and Zaz [82], Salamanca et al [83] and Perez et al [84] published results on detecting PV modules in aerial visual images, and many studies used different techniques to detect and segment PV module boundaries in aIRT data. Table 3 shows the studies related to DIP and DL algorithms.…”

Section: Detection Of Pv Modulesmentioning

confidence: 99%

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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“…The preprocessing steps are important for IR images as well. Salamanca, Merchán, and García [21] use the grey-level cooccurrence matrix to identify the location of the solar panels in IR images of the operating photovoltaic plants.…”

Section: A Image Analysis In Pvmentioning

confidence: 99%

Encoder-decoder semantic segmentation models for electroluminescence images of thin-film photovoltaic modules

Sovetkin¹,

Achterberg²,

Weber³

et al. 2020

Preprint

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We consider a series of image segmentation methods based on the deep neural networks in order to perform semantic segmentation of electroluminescence (EL) images of thin-film modules. We utilize the encoder-decoder deep neural network architecture. The framework is general such that it can easily be extended to other types of images (e.g. thermography) or solar cell technologies (e.g. crystalline silicon modules). The networks are trained and tested on a sample of images from a database with 6000 EL images of Copper Indium Gallium Diselenide (CIGS) thin film modules. We selected two types of features to extract, shunts and so called "droplets". The latter feature is often observed in the set of images. Several models are tested using various combinations of encoder-decoder layers, and a procedure is proposed to select the best model. We show exemplary results with the best selected model. Furthermore, we applied the best model to the full set of 6000 images and demonstrate that the automated segmentation of EL images can reveal many subtle features which cannot be inferred from studying a small sample of images. We believe these features can contribute to process optimization and quality control.

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On the detection of solar panels by image processing techniques

Cited by 15 publications

References 8 publications

SSD vs. YOLO for Detection of Outdoor Urban Advertising Panels under Multiple Variabilities

SSD vs. YOLO for Detection of Outdoor Urban Advertising Panels under Multiple Variabilities

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

Encoder-decoder semantic segmentation models for electroluminescence images of thin-film photovoltaic modules

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