Number Of Defect Samples Research Articles

A Feature-Oriented Reconstruction Method for Surface-Defect Detection on Aluminum Profiles

The number of defect samples on the surface of aluminum profiles is small, and the distribution of abnormal visual features is dispersed, such that the existing supervised detection methods cannot effectively detect undefined defects. At the same time, the normal texture of the aluminum profile surface presents non-uniform and non-periodic features, and this irregular distribution makes it difficult for classical reconstruction networks to accurately reconstruct the normal features, resulting in low performance of related unsupervised detection methods. Aiming at such problems, a feature-oriented reconstruction method of unsupervised surface-defect detection method for aluminum profiles is proposed. The aluminum profile image preprocessing stage uses techniques such as boundary extraction, background removal, and data normalization to process the original image and extract the image of the main part of the aluminum profile, which reduces the influence of irrelevant data features on the algorithm. The essential features learning stage precedes the feature-optimization module to eliminate the texture interference of the irregular distribution of the aluminum profile surface, and image blocks of the area images are reconstructed one by one to extract the features through the mask. The defect-detection stage compares the structural similarity of the feature images before and after the reconstruction, and comprehensively determines the detection results. The experimental results improve detection precision by 1.4% and the F1 value by 1.2% over the existing unsupervised methods, proving the effectiveness and superiority of the proposed method.

Unsupervised fabric defect detection based on multiscale image reconstruction and structural similarity assessment

AbstractFabric defect detection is a crucial aspect of the textile industry. Currently, deep learning methods have demonstrated exceptional performance in fabric defect detection tasks. However, their performance is greatly affected by the number of defect samples, which is a challenge to obtain during actual production. To address this issue, this article proposes an unsupervised anomaly detection method for fabric defects using image reconstruction networks. This method only requires defect‐free samples for training. During the training phase, the model compresses defect‐free samples to obtain a low‐dimensional manifold and reconstruct them. During the inference phase, the method assesses whether a sample is defective by calculating the reconstruction error between the input and output images, and locates the defect region by computing the difference in various patches. Furthermore, since fabric contains rich texture features, with high correlation between neighbouring pixels, a structure similarity index measure combined with mean absolute error is introduced to evaluate the reconstruction error, which enhances the model's representation ability for defect‐free samples. Additionally, considering the diverse texture backgrounds in fabric, a multiscale reconstruction module is designed to optimise the reconstruction effect. Experimental results demonstrate that compared with other related approaches, the proposed method achieves high accuracy (image‐based area under the curve (AUC) up to 98.2% and pixel‐based AUC up to 97.3%) on multiple datasets and has good generalisation ability for different fabric textures.

Results of bacterial analysis of foodstuffs and swabs from working surfaces in food manufacturing and marketing facilities in Bosnian-Podrinje Canton Goražde

<p>Introduction. According to the Consumer and Food Safety Act, the term foodstuffs is referred to as any substance used for food or drink, either processed or unprocessed. Food safety is referred to as hygiene food safety as well as the safety of food composition (energy giving, body building and protective food components). The aim of the study is to examine the bacteriological quality of samples of foodstuffs and swabs from working surfaces in food manufacturing and marketing facilities in Bosnian-Podrinje Canton (BPC) from 2011 to 2015, as well as to determine whether the increase in the frequency of supervision leads to the decrease in the number of defective samples. Methods.The study encompassed three municipalities of BPC: Goražde, Prača and Ustikolina. Microbiological quality of samples was tested in the accredited microbiology laboratory of the Public Health Institute of the Sarajevo Canton, and partly in the laboratories of the Public Health Institute of BPC. For a retrospective view of the obtained data, Central laboratory protocols from the last five years were used. Results. The greatest number of samples was collected in catering establishments in Goražde Municipality. The number of analyzed samples increased during five-year period: from 51 food and 69 swab samples in 2011 to 150 food and 270 surface swab samples in 2015. The number of inadequate samples decreased simultaneously from 7.27% to 3.33%. In the majority of samples, aerobic mesophilic bacteria (37) and saprophytic cocci (25) were isolated, while Streptoccocus faecalis and sulphitoreducing Clostridiae were found only in one sample. The number of samples in which bacteria were isolated decreased in five-year period: from 38 samples in 2011 to 15 samples in 2015. Conclusion. It has been shown that the more frequent control of the food manufacturing and marketing facilities leads to better results, that is to the reduction in the number of bacteriological defective samples. Food safety is not alarming in BPC.</p>

Multifeature, Sparse-Based Approach for Defects Detection and Classification in Semiconductor Units

Automated inspection systems play an important role in manufacturing to guarantee higher quality and reduce production costs. In the semiconductor manufacturing industry, assembly and testing processes are getting more complex, resulting in a greater tendency of defects to impact the production process. These defects can cause field failures and can result in customer dissatisfactions and returns. Currently available defect detection and classification systems are customized and hard-wired to the detection of particular classes of defects and cannot deal with new unknown classes of defects. This issue is aggravated by the very small sample size of available anomalies for learning, by the data imbalance problem, since the number of defective samples is significantly much smaller than the number of normal samples, and by the presence of noise. This paper presents a novel multifeature, sparse-based defect detection and classification approach that uses the stacking concept to enhance the classification accuracy. The stacking-based classifier is augmented with a novel adaptive over/downsampling technique to deal with the data imbalance problem. A new pruning technique is proposed to eliminate bad base learners. Shortage of defective units, similarities within different classes of defects, wide variation within the same defect class, and data imbalance are the basic challenges to deal with. Experimental results on real-world data from Intel show that the proposed approach results in a high classification accuracy as compared with the existing methods. Note to Practitioners —The basic motivation of this paper is to design an automated cost-effective, adaptive, and intelligent defect detection and classification system that is easy to train using a small-size sample set of defects and that is robust to noise. The system is scalable in terms of the numbers and types of defects and features, which leads to a shorter development cycle. The presented system is immediately applicable to different types of defects. Inputs of the system are grayscale images. These images are processed to perform defects detection, features extraction, and classification.

Bootstrap sampling technique applied to the PCB golden fingers defect classification study

Surface defects classification techniques commonly used in the literature are statistical methods (such as the Bayesian classifier, the linear discrimination function classifier, the minimum distance classifier and the nearest neighbour classifier) and non-statistical methods (such as neural networks and fuzzy logic). The PCB industry in Taiwan is already a small volume with a large variety type of production. If the number of defect samples is too few, it is difficult to apply any of the above-mentioned techniques for classification. The Bootstrap sampling technique is proposed for the generation of enough samples to determine population parameters for golden finger defect classification. An experiment was conducted to demonstrate the application of the technique. The tree classifier was used as the classifier in this study. The results showed that the classification correctness reached 97.87%, as opposed to 84.46% when other classifiers were used. The results provide an effective solution for the defects classification problem with a small sample situation.