Micro-crack detection of solar cell based on adaptive deep features and visual saliency

Qian, Xiaoliang; Li, Jing; Zhang, Jianwei; Zhang, Wenhao; Yue, Weichao; Wu, QingE; Zhang, Huanlong; Wu, Yuanyuan; Wang, Wei

doi:10.1108/sr-05-2019-0124

Cited by 7 publications

(5 citation statements)

References 38 publications

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“…The authors resulted 96.1% Se, 96.3% Sp, 96.3% Ac and 95.9% Pr using their proposed crack detection method. Qian et al (2020) [11] constructed visual saliency algorithm using deep learning features to identify the micro cracks in solar panel images. The authors used supervised training algorithm for the classification of deep features to locate the cracked pixels in the test solar panel images.…”

Section: Literature Surveymentioning

confidence: 99%

Detection of Cracks in Solar Panel Images Using Complex Wavelet Transform and ANFIS Classification Method

Perarasi¹,

Ramadas²

2023

Preprint

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The performance efficiency of the installed solar panels in larger environmental area is often degrading by cracks. Hence, there is a need for detecting these cracks in solar panel images is a vital task to improve the efficiency of the solar panel output. In this paper, the cracked and non-cracked solar panels are classified using Complex Wavelet Transform (CWT) based Adaptive Neuro Fuzzy Inference System (ANFIS) classification method. The Gaussian filter is applied on the solar panel image to detect and remove the blurs in the cracked pixel edges. The preprocessed image is now decomposed into sub band images using CWT. The texture and statistical features are computed from the decomposed sub band images and these features are classified using ANFIS classifier which classifies the solar panel image into either cracked or non-cracked image. The segmentation algorithm is now applied on the classified cracked solar panel image to detect the cracked pixels. This developed crack detection approach is tested on large number of solar panel images and their experimental results are analyzed in terms of sensitivity, specificity, accuracy and precision. The proposed solar panel crack detection system attains 97.6% of average Se, 97.6% of average Sp, 98.2% of average Ac and 97.9% of average Pr. These experimental results of the proposed method is evaluated by k-fold cross validation method and the experimental results are compared with other similar methods.

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Section: Literature Surveymentioning

confidence: 99%

Detection of Cracks in Solar Panel Images Using Complex Wavelet Transform and ANFIS Classification Method

Perarasi¹,

Ramadas²

2023

Preprint

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“…In the case of solar cell inspection, anomaly detection approaches have been proposed in Qian et al [ 34 , 43 ], where they train a Stacked Denoising AutoEncoder (SDAE) to extract features from defect-free samples using the sliding window method. In Qian et al [ 34 ], they extend the network architecture with a pre-trained VGG16 network that works as an additional feature extractor.…”

Section: Related Workmentioning

confidence: 99%

Anomaly Detection and Automatic Labeling for Solar Cell Quality Inspection Based on Generative Adversarial Network

Balzategui

Eciolaza

Maestro-Watson

2021

Sensors

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Quality inspection applications in industry are required to move towards a zero-defect manufacturing scenario, with non-destructive inspection and traceability of 100% of produced parts. Developing robust fault detection and classification models from the start-up of the lines is challenging due to the difficulty in getting enough representative samples of the faulty patterns and the need to manually label them. This work presents a methodology to develop a robust inspection system, targeting these peculiarities, in the context of solar cell manufacturing. The methodology is divided into two phases: In the first phase, an anomaly detection model based on a Generative Adversarial Network (GAN) is employed. This model enables the detection and localization of anomalous patterns within the solar cells from the beginning, using only non-defective samples for training and without any manual labeling involved. In a second stage, as defective samples arise, the detected anomalies will be used as automatically generated annotations for the supervised training of a Fully Convolutional Network that is capable of detecting multiple types of faults. The experimental results using 1873 Electroluminescence (EL) images of monocrystalline cells show that (a) the anomaly detection scheme can be used to start detecting features with very little available data, (b) the anomaly detection may serve as automatic labeling in order to train a supervised model, and (c) segmentation and classification results of supervised models trained with automatic labels are comparable to the ones obtained from the models trained with manual labels.

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“…Finally, they introduced visual saliency into micro-crack detection. 10 Kim et al 11 carried out the relevant solar panel defect detection experiments in the outdoor environment and obtained the optimal scheme of solar panel detection in the experiment. In terms of real-time sensing technology and visual perception, Xiang et al 12 used tactile devices to accurately sense people and obtain outdoor remote switching server (RSS) maps.…”

Section: Related Workmentioning

confidence: 99%

ISEE: Industrial Internet of Things perception in solar cell detection based on edge computing

Zhao

et al. 2021

International Journal of Distributed Sensor Networks

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The photovoltaic industry is a strategic and sunrise industry with international competitive advantages. Driven by policy guidance and market demand, the new energy industry represented by the photovoltaic industry has been a significant emerging industry in developing the national economy and people’s livelihood. Stable photovoltaic power generation capacity supply is a critical issue in the photovoltaic industry. With the popularization of industrial Internet technology and Internet of things technology, more and more academic and industrial circles begin to introduce new technologies to provide the latest research results and solutions for the photovoltaic industry. Electroluminescence is a standard detection method for photovoltaic production in the application of solar energy production. This method uses human vision to detect whether the solar silicon unit is defective. In this article, due to the three core pain points in traditional electroluminescence detection: low efficiency of offline identification, low accuracy and accuracy of data detection, and no online diagnosis and prediction, we carry out ISEE research based on edge computing unit. ISEE uses the edge device to collect the real-time video image of the solar panel through the camera. Then it uses the powerful neural network processing unit module of the edge computing unit, combined with the convolutional neural network algorithm transplanted to the edge, to detect the defects of solar panels in real time. It completes the research on intelligent detection of photovoltaic power generation production defects based on the Internet of Things. After a large number of experimental design verification, ISEE effectively improves the automation degree and identification accuracy in the production and detection process of solar photovoltaic cells and reduces the cost of operation and maintenance. The accuracy rate reaches 93.75%, which has significant theoretical research significance and practical application value.

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Micro-crack detection of solar cell based on adaptive deep features and visual saliency

Cited by 7 publications

References 38 publications

Detection of Cracks in Solar Panel Images Using Complex Wavelet Transform and ANFIS Classification Method

Detection of Cracks in Solar Panel Images Using Complex Wavelet Transform and ANFIS Classification Method

Anomaly Detection and Automatic Labeling for Solar Cell Quality Inspection Based on Generative Adversarial Network

ISEE: Industrial Internet of Things perception in solar cell detection based on edge computing

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