Detection of surface defects on solar cells by fusing Multi-channel convolution neural networks

Zhang, Xiong; Hao, Yawen; Shangguan, Hong; Zhang, Pengcheng; Wang, Anhong

doi:10.1016/j.infrared.2020.103334

Cited by 57 publications

(29 citation statements)

References 19 publications

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“…Or we explicitly train a Fully Convolutional Network to perform pixel-wise classification [ 29 ]. Additionally, other researchers have also proposed other types of defect location, using bounding boxes [ 30 , 31 ], or by visualizing the activation maps from the last network layer [ 25 , 32 ].…”

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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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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“…The combined usage of short-and long-term deep features enables effective detection of various micro-cracks. Zhang et al [11] proposed a solar cell surface defect detection method based on a model that fuses the Faster R-CNN and the R-FCN. They improved the detection accuracy through the complementary fusion of detection results of the Faster R-CNN and R-FCN models.…”

Section: Related Researchmentioning

confidence: 99%

Deep-Learning-Based Defect Evaluation of Mono-Like Cast Silicon Wafers

2021

Photonics

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Solar cells based on mono-like cast silicon (MLC-Si) have been attracting increasing attention in the photovoltaic (PV) market due to their high energy conversion efficiency and low cost. As in the production of monocrystalline silicon (MC-Si) and polycrystalline silicon (PC-Si) cells, various defects will inevitably occur during the production process of MLC-Si cells. Although computer vision technology has been employed for defect detection in the production processes, it is still difficult to achieve high accuracy in detecting defects in PV cells using traditional machine vision methods due to defect similarity and complex background. To address this challenge, a deep-learning-based quality assessment algorithm of MLC-Si wafers is proposed. Focusing on the dislocation defects, four different deep learning models are used to conduct migration learning and selected different optimizers (ADAM and SGDM) are used to optimize the network models, achieving good results in evaluating and comparing the quality of ML-Si wafers. On this basis, an improved network model MVGG-19 based on the VGG-19 is designed to improve the prediction accuracy further. The experimental results show that the prediction error of the improved network model is reduced by 63% (compared with VGG-19) and the reasoning speed reaches 10.22 FPS, indicating good detection performance.

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“…In [13], a public dataset of solar cells is provided that contains 2,624 solar cell images and two approaches are proposed to classify the EL images. In [14], a fusion model of Faster R-CNN and R-FCN is proposed to detect solar cell surface defects. In [15], an efficient method for defects inspection has been proposed that leverages the multi-attention network and the hybrid loss to improve the performance.…”

Section: Introductionmentioning

confidence: 99%

“…The details of the previous work [12][13][14][15][16][17][18][19][20][21][22][23][24][25] are presented in Table 1. The limitations of these solutions can be summarized as follows: (1) Most images used in the previous studies are collected during the factory inspection and the resolution of the images captured during the factory inspection is generally much higher than those collected during the field inspection using the unmanned aerial vehicle (UAV).…”

Section: Introductionmentioning

confidence: 99%

Deep Learning-Based Algorithm for Multi-Type Defects Detection in Solar Cells with Aerial EL Images for Photovoltaic Plants

Tang¹,

Yang²,

Yan³

2022

Computer Modeling in Engineering &Amp; Sciences

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Defects detection with Electroluminescence (EL) image for photovoltaic (PV) module has become a standard test procedure during the process of production, installation, and operation of solar modules. There are some typical defects types, such as crack, finger interruption, that can be recognized with high accuracy. However, due to the complexity of EL images and the limitation of the dataset, it is hard to label all types of defects during the inspection process. The unknown or unlabeled create significant difficulties in the practical application of the automatic defects detection technique. To address the problem, we proposed an evolutionary algorithm combined with traditional image processing technology, deep learning, transfer learning, and deep clustering, which can recognize the unknown or unlabeled in the original dataset defects automatically along with the increasing of the dataset size. Specifically, we first propose a deep learning-based features extractor and defects classifier. Then, the unlabeled defects can be classified by the deep clustering algorithm and stored separately to update the original database without human intervention. When the number of unknown images reaches the preset values, transfer learning is introduced to train the classifier with the updated database. The fine-tuned model can detect new defects with high accuracy. Finally, numerical results confirm that the proposed solution can carry out efficient and accurate defect detection automatically using electroluminescence images.

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Detection of surface defects on solar cells by fusing Multi-channel convolution neural networks

Cited by 57 publications

References 19 publications

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

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

Deep-Learning-Based Defect Evaluation of Mono-Like Cast Silicon Wafers

Deep Learning-Based Algorithm for Multi-Type Defects Detection in Solar Cells with Aerial EL Images for Photovoltaic Plants

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