Crack Detection Of Eggshell Featuring An Improved Anisotropic Diffusion Filter And Support Vector Machine

Cracks on eggshell are categorized into two types: (i) macro-crack, and (ii) micro-crack. Unlike macro-crack, the detection of micro-crack is very difficult and challenging since this type of defect is invisible to naked eyes. This problem has been partially solved by utilizing a custom made candling light in the background illumination set-up. Even though this has improved the visibility of micro-crack pixels, however this imaging technique has also enhanced anomalies and other unwanted pixels, leading to a very cluttered and noisy images. A three-stage window-free method was proposed to solve this problem. In the first stage, line enhancement was implemented in order to enhance the quality of line in the image. Next, the crack enhancement was performed using an improved anisotropic diffusion filter. In this case, cracks are characterized by pixels having high intensity and high gradient values. Using these characteristics, the detection system has been developed to inspect eggshells and classify them into one of the following three possible classes: (i) intact, (ii) micro-crack, and (iii) macro-crack. In the third stage, a modified double thresholding was employed to further highlight crack pixels. Results indicate that the proposed method is competitive when compared with existing techniques and achieved better performance in terms of FOM. On average the method has resulted in FOM of 0.73 compared to 0.67, 0.57 and 0.42 produced by the original and two recent variants of anisotropic diffusion filter for crack enhancement, and 0.52, 0.68 and 0.48 produced by Otsu, Sobel and Canny techniques for image segmentation. Meanwhile the classifications has been performed using the state of the art twin bounded support vector machine (TBSVM) and the results have been compared with the standard support vector machine (SVM) utilizing three different approaches: (i) one-versus-all (OVA), (ii) one-versus-one (OVO), and (iii) directed acyclic graph (DAG). Results reveal that DAG outperforms OVA and OVO with sensitivity, specificity and accuracy averaging at 93.1%, 96.5% and 93.0% for TBSVM compared to 90.7%, 95.4% and 90.7% for standard SVM. Meanwhile the ROC performance indicates that this classifier can distinguish between intact and macro-crack samples with 100% certainty. The performance decreases insignificantly when distinguishing intact from micro-crack and micro-crack from macro-crack samples. Therefore, these results suggest that the proposed detection system is useful and effective for applications in egg processing

Anomaly detection and automatic labeling for solar cell quality inspection based on Generative Adversarial Network

Quality inspection applications in industry are required to move towards a zero-defect manufacturing scenario, withnon-destructive inspection and traceability of 100 % of produced parts. Developing robust fault detection and classification modelsfrom the start-up of the lines is challenging due to the difficulty in getting enough representative samples of the faulty patternsand the need to manually label them. This work presents a methodology to develop a robust inspection system, targeting thesepeculiarities, in the context of solar cell manufacturing. The methodology is divided into two phases: In the first phase, an anomalydetection model based on a Generative Adversarial Network (GAN) is employed. This model enables the detection and localizationof anomalous patterns within the solar cells from the beginning, using only non-defective samples for training and without anymanual labeling involved. In a second stage, as defective samples arise, the detected anomalies will be used as automaticallygenerated annotations for the supervised training of a Fully Convolutional Network that is capable of detecting multiple types offaults. The experimental results using 1873 EL images of monocrystalline cells show that (a) the anomaly detection scheme can beused to start detecting features with very little available data, (b) the anomaly detection may serve as automatic labeling in order totrain a supervised model, and (c) segmentation and classification results of supervised models trained with automatic labels arecomparable to the ones obtained from the models trained with manual labels.Comment: 20 pages, 10 figures, 6 tables. This article is part of the special issue "Condition Monitoring, Field Inspection and Fault Diagnostic Methods for Photovoltaic Systems" Published in MDPI - Sensors: see https://www.mdpi.com/journal/sensors/special_issues/Condition_Monitoring_Field_Inspection_and_Fault_Diagnostic_Methods_for_Photovoltaic_System

Solar Cell Cracks and Finger Failure Detection Using Statistical Parameters of Electroluminescence Images and Machine Learning

A wide range of defects, failures, and degradation can develop at different stages in the lifetime of photovoltaic modules. To accurately assess their effect on the module performance, these failures need to be quantified. Electroluminescence (EL) imaging is a powerful diagnostic method, providing high spatial resolution images of solar cells and modules. EL images allow the identification and quantification of different types of failures, including those in high recombination regions, as well as series resistance-related problems. In this study, almost 46,000 EL cell images are extracted from photovoltaic modules with different defects. We present a method that extracts statistical parameters from the histogram of these images and utilizes them as a feature descriptor. Machine learning algorithms are then trained using this descriptor to classify the detected defects into three categories: (i) cracks (Mode B and C), (ii) micro-cracks (Mode A) and finger failures, and (iii) no failures. By comparing the developed methods with the commonly used one, this study demonstrates that the pre-processing of images into a feature vector of statistical parameters provides a higher classification accuracy than would be obtained by raw images alone. The proposed method can autonomously detect cracks and finger failures, enabling outdoor EL inspection using a drone-mounted system for quick assessments of photovoltaic fields.</p

Road crack detection using adaptive multi resolution thresholding techniques

Machine vision is very important for ensuring the success of intelligent transportation systems, particularly in the area of road maintenance. For this reason, many studies had been focusing on automatic image-based crack detection as a replacement for manual inspection that had depended on the specialist’s knowledge and expertise. In the image processing technique, the pre-processing and edge detection stages are important for filtering out noises and in enhancing the quality of the edges in the image. Since threshold is one of the powerful methods used in the edge detection of an image, we have therefore proposed a modified Otsu-Canny Edge Detection Algorithm in the selection of the two threshold values as well as implemented a multi-resolution level fixed partitioning method in the analysis of the global and local threshold values of the image. This is then followed by a statistical measure in selecting the edge image with the best global threshold. This study had utilized the road crack image dataset that were obtained from Crackforest. The results had revealed the proposed method to not only perform better than the conventional Canny edge detection method but had also shown the maximum value derived from the local threshold of 5x5 partitioned image outperforming the other partitioned scales

A lightweight network for photovoltaic cell defect detection in electroluminescence images based on neural architecture search and knowledge distillation

Nowadays, the rapid development of photovoltaic(PV) power stations requires increasingly reliable maintenance and fault diagnosis of PV modules in the field. Due to the effectiveness, convolutional neural network (CNN) has been widely used in the existing automatic defect detection of PV cells. However, the parameters of these CNN-based models are very large, which require stringent hardware resources and it is difficult to be applied in actual industrial projects. To solve these problems, we propose a novel lightweight high-performance model for automatic defect detection of PV cells in electroluminescence(EL) images based on neural architecture search and knowledge distillation. To auto-design an effective lightweight model, we introduce neural architecture search to the field of PV cell defect classification for the first time. Since the defect can be any size, we design a proper search structure of network to better exploit the multi-scale characteristic. To improve the overall performance of the searched lightweight model, we further transfer the knowledge learned by the existing pre-trained large-scale model based on knowledge distillation. Different kinds of knowledge are exploited and transferred, including attention information, feature information, logit information and task-oriented information. Experiments have demonstrated that the proposed model achieves the state-of-the-art performance on the public PV cell dataset of EL images under online data augmentation with accuracy of 91.74% and the parameters of 1.85M. The proposed lightweight high-performance model can be easily deployed to the end devices of the actual industrial projects and retain the accuracy.Comment: 12 pages, 7 figure

Road Crack Detection Using Adaptive Multi Resolution Thresholding Techniques

Machine vision is very important for ensuring the success of intelligent transportation systems, particularly in the area of road maintenance. For this reason, many studies had been focusing on automatic image-based crack detection as a replacement for manual inspection that had depended on the specialist's knowledge and expertise. In the image processing technique, the pre-processing and edge detection stages are important for filtering out noises and in enhancing the quality of the edges in the image. Since threshold is one of the powerful methods used in the edge detection of an image, we have therefore proposed a modified Otsu-Canny Edge Detection Algorithm in the selection of the two threshold values as well as implemented a multi-resolution level fixed partitioning method in the analysis of the global and local threshold values of the image. This is then followed by a statistical measure in selecting the edge image with the best global threshold. This study had utilized the road crack image dataset that were obtained from Crackforest. The results had revealed the proposed method to not only perform better than the conventional Canny edge detection method but had also shown the maximum value derived from the local threshold of 5 × 5 partitioned image outperforming the other partitioned scales

Automatic Road Crack Segmentation Using Thresholding Methods

Maintenance of good condition of roads are very essential to the economy and everyday life of people in a every country. Road cracks are one of the important indicators that show degradations of road surfaces. Inspection of roads that have been done manually took a very long time and tedious. Hence, an automatic road crack segmentation using thresholding methods have been proposed in this study. In this study, ten road crack images have been pre-processed as an initial step. Then, normalization techniques, L1-Sqrt norm have been applied onto images to reduce the variation of intensities that skewed to the right. Then, thresholding methods, Otsu and Sauvola methods have been used to binarize the images.  From the experiment of ten road crack images that have been done, normalization technique, L1-Sqrt norm can help to increase performance of road crack segmentation for Otsu and Sauvola methods. The results also show that Sauvola method outperform Otsu method in detecting road cracks

Segmentation of Photovoltaic Module Cells in Electroluminescence Images

High resolution electroluminescence (EL) images captured in the infrared spectrum allow to visually and non-destructively inspect the quality of photovoltaic (PV) modules. Currently, however, such a visual inspection requires trained experts to discern different kinds of defects, which is time-consuming and expensive. Automated segmentation of cells is therefore a key step in automating the visual inspection workflow. In this work, we propose a robust automated segmentation method for extraction of individual solar cells from EL images of PV modules. This enables controlled studies on large amounts of data to understanding the effects of module degradation over time-a process not yet fully understood. The proposed method infers in several steps a high-level solar module representation from low-level edge features. An important step in the algorithm is to formulate the segmentation problem in terms of lens calibration by exploiting the plumbline constraint. We evaluate our method on a dataset of various solar modules types containing a total of 408 solar cells with various defects. Our method robustly solves this task with a median weighted Jaccard index of 94.47% and an $F_1$ score of 97.54%, both indicating a very high similarity between automatically segmented and ground truth solar cell masks