Automated Visual Inspection Algorithms

With machine learning (ML), we see the potential to better harness the intelligence and decision-making abilities of human inspectors performing manual visual inspection (MVI) and apply this to automated visual inspection (AVI) with the inherent improvements in throughput and consistency. This article is intended to capture current experience with this new technology and provides points to consider for successful application to AVI of injectable drug products. The technology is available today for such AVI applications. Machine vision companies have integrated ML as an additional visual inspection tool with minimal upgrades to existing hardware. Studies have demonstrated superior results in defect detection and reduction in false rejects, when compared with conventional inspection tools. ML implementation does not require modifications to current AVI qualification strategies. The utilization of this technology for AVI will accelerate recipe development by use of faster computers rather than by direct human configuration and coding of vision tools. By freezing the model developed with artificial intelligence tools and subjecting it to current validation strategies, assurance of reliable performance in the production environment can be achieved.

One of the challenges faced by the users of automated visual inspection (AVI) systems is how to efficiently upgrade the legacy systems to inspect the new components introduced into the assembly lines. If the AVI systems are not flexible enough to accommodate new components, they will be rendered obsolete even by small changes in the product being inspected. The overall objective of the research presented in this paper is to produce the methodological basis that will result in the development of highly reconfigurable AVI systems. In this paper, we focus on part of this overall development, the adaptation of preexisting inspection algorithms to inspect similar components introduced into the assembly line. While this paper bases its development and discussion on the inspection of surface mounted devices (SMDs), the proposed methodology is general enough to be applicable to a broad range of inspection problems. In this paper, we present a methodology that would allow the automation of the refinement of AVI algorithms. In particular, the proposed method identifies a set of components, or cluster of components, for which a particular set of inspection features or algorithms, renders a certain level of inspection reliability. This is particularly useful for adapting preexisting systems to inspect new components, especially when the characteristics of the new components are similar to those of components already inspected by the inspection system. We applied this methodology to a case of study of the inspection of SMDs.

Visual inspection becomes an important role when determining the quality of the conditions of production. Manual visual inspection carried out by relying on human senses, especially the eyes, the weakness is perspective and human's perspective may be different from each other, the quality result can be different. Utilization of the camera as a sensor and computer system as a major component of decision-making is the latest technology that is used as an automatic visual inspection replace human tasks. This research aims to make a visual inspection system that utilizes a web camera and mini PC (personal mini computare) Raspberry PI to identify disability packaging beverage cans. The system that is made has been tested to identify defects tin side of the inner surface of the cover packaging of canned drinks without color or silver color. The result is, the system is able to distinguish defective cans and the cans that are not defective with an accuracy of 66, 67%. We hope this research can be developed at a later stage so that automatic a visual inspection can be realized.

In pattern recognition supervised learning algorithms has been used to model the relationships among the characteristics of a group of objects (or patterns) and their class labels. This model is build based on a set of training images that have been labelled by an expert in a specific problem domain. In general, the labelling process consists in defining the object's class label as well as demarcating the precise part of the image in which it is located the object of interest. In many computer vision applications, for example in automatic visual inspection, the labelling process may be a laborious task which requires an extensive work, it could even become impractical when a large number of training images are needed to train the pattern recognition system. This limitation has led to the development of new learning algorithms that allow some ambiguity or "weakness" in the way class labels are assigned. These algorithms are known as weakly supervised learning algorithms. In this paper we present an evaluation of different weakly supervised learning paradigms grouped according to the representation of the object of interest (in one or several characteristic vectors) associated to its class label (single or multiple label). We perform different experiments in the automatic visual inspection context, using a benchmark dataset of texture images with artificial defects. We test different weakly supervised learning algorithms. Results show that representing an object using multiple instances, presents better results in the identification of defective texture image.

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The issue of adaptability in the design of automated visual inspection (AVI) systems has been a major road block in the application of AVI technology to manufacturing industry. The objective of an adaptable AVI system is to reduce system tuning time while maintaining the quality of inspection. In the authors’ previous work (Int J Adv Manuf Technol 59:655–667, 2012), we have proposed a novel adaptable AVI scheme, with which an AVI system can be trained online to accommodate new patterns. As such, the system can be deployed to undertake new inspection tasks or adapt to new operation conditions without excessive offline training efforts. The research presented in this two-part article is our further development of the adaptable AVI scheme, namely A2VIS. In this research, we focus on elaborating the concept, designing the structure, implementing the functions, and evaluating the performance of A2VIS in a systemic perspective. In part I, the proposed A2VIS is presented as a generic scheme at the conceptual level, with respect to the system components and structure, operating procedure, and performance measures. The key element in the design of the A2VIS is an adaptable inspection model that integrates subject localization, feature extraction, and classification functions as trainable submodels. In part I of this paper, we developed a generic tool for an adaptable AVI system without specifying the use of particular machine vision and machine learning techniques. Interested readers may choose techniques for implementing the proposed scheme according to their own preferences. In part II of this article, we focus on the system implementation and evaluation of A2VIS using such techniques as template matching, principal component analysis (PCA), and support vector machine (SVM).

This paper presents a defect detection method based on two dimensional Fourier transform. In automatic visual inspection in factories, the displacement between two inspected images contains both parallel shift and rotation, which causes the inspection process to be highly time-consuming. In the proposed method, the subtraction between Fourier transform magnitudes of two input images is calculated for detecting differences between the corresponding pixels. Due to the shift- invariant nature of magnitudes in Fourier transform spectrum, the method does not require an image registration process for parallel shift. For images rotated by a small angle, a correlation based on one dimensional Fourier transform is calculated to estimate the angle.Detection experiments are executed on Mac Pro 2.66GHz using images with 256 × 256 to 4000 × 2672 pixels. For the largest resolution images, the method detects differences in 2.39 seconds. The experimental results show that the proposed method has sufficient detection precision and faster computation speed than existing visual inspection methods. Estimation of specular reflection on PCB surfaces and further acceleration of the calculation are to be implemented.

Over the recent years, developing a reliable auto-mated visual inspection system/approach for manufacturing and industry sectors which are moving toward smart manufacturing operations faces lots of significant challenges. Traditional visual inspection techniques which are developed based on manually extracted features, can rarely be generalized and have shown weak performance in real applications in different industries. In this paper, we propose a novel and automated visual inspection system which can outperform the statistical methods in terms of detection and the quantification of anomalies in image data for performing critical industrial tasks such as detecting micro scratches on product. In particular, an end-to-end UNet-based fully convolutional neural network for automated defect detection in industrial surfaces is designed and developed. The proposed network has the capability to accept raw images as input and the output is pixel-wise masks. In order to avoid overfitting and improve the model generalization, we use real-time data augmentation approach during our training phase. To evaluate the performance of the proposed model, we use a publicly available data set containing ten different types of textured-surfaces with their associated weakly annotated masks. The findings indicate that despite working with roughly annotated labels, our results are in agreement with previous works and show improvements regarding the detection time.

Virtualisation of Product Development may be interpreted in two ways: The virtualisation of the “Factory” or the virtualisation of the product development process. In order to virtualise product development, general development environments should be developed. In this talk, I will present some contributions to such environments: TrollCreek – a Case Based Reasoning (CBR) environment for utilization of knowledge and experience. XPLANO a Rule Based system for Automatic Process Planning. TrollEye – an Automatic Visual Inspection (AVI) environment using Machine Vision.

With a drastic growth in the development of Automated Visual Inspection (AVI) systems in the industries, the capabilities of such applications to aid human inspectors for anomaly localization and identification have also increased. However, some issues with anomaly detection and classification in AVI systems are that such anomalies are rare in occurrence and exhibit behaviours that are unique to the application. Hence, these anomaly datasets are small and imbalanced, and a robust framework is required for such datasets. This paper proposes a Salient Textural Anomaly Proposal (STAP) framework to generate and classify salient textural proposals of regions of anomalies on metal surfaces. These anomalies have both salient and texture characteristics that are dependent on the properties of the metal surface. Furthermore, when observed across different lighting conditions, the anomalies in this AVI anomaly dataset have a small inter-class variance and large intraclass variance. The proposed STAP framework uses a Fourier transformation based technique to generate proposals of salient and textural anomaly regions. Transfer learning of Convolutional Neural Network (CNN) learned from a large dataset is used to train a linear Support Vector Machine (SVM) to classify the generated proposals. The proposed STAP framework performs the best when compared to state-of-the-art object recognition techniques on an AVI anomaly industrial dataset that has salient and texture anomalies on metal surfaces.

PurposeHeritage is the latent part of a sustainable built environment. Conservation and preservation of heritage is one of the United Nations' (UN) sustainable development goals. Many social and natural factors seriously threaten heritage structures by deteriorating and damaging the original. Therefore, regular visual inspection of heritage structures is necessary for their conservation and preservation. Conventional inspection practice relies on manual inspection, which takes more time and human resources. The inspection system seeks an innovative approach that should be cheaper, faster, safer and less prone to human error than manual inspection. Therefore, this study aims to develop an automatic system of visual inspection for the built heritage.Design/methodology/approachThe artificial intelligence-based automatic defect detection system is developed using the faster R-CNN (faster region-based convolutional neural network) model of object detection to build an automatic visual inspection system. From the English and Dutch cemeteries of Surat (India), images of heritage structures were captured by digital camera to prepare the image data set. This image data set was used for training, validation and testing to develop the automatic defect detection model. While validating this model, its optimum detection accuracy is recorded as 91.58% to detect three types of defects: “spalling,” “exposed bricks” and “cracks.”FindingsThis study develops the model of automatic web-based visual inspection systems for the heritage structures using the faster R-CNN. Then it demonstrates detection of defects of spalling, exposed bricks and cracks existing in the heritage structures. Comparison of conventional (manual) and developed automatic inspection systems reveals that the developed automatic system requires less time and staff. Therefore, the routine inspection can be faster, cheaper, safer and more accurate than the conventional inspection method.Practical implicationsThe study presented here can improve inspecting the built heritages by reducing inspection time and cost, eliminating chances of human errors and accidents and having accurate and consistent information. This study attempts to ensure the sustainability of the built heritage.Originality/valueFor ensuring the sustainability of built heritage, this study presents the artificial intelligence-based methodology for the development of an automatic visual inspection system. The automatic web-based visual inspection system for the built heritage has not been reported in previous studies so far.

A novel generalization of the gray-scale histogram and its application to the automated visual measurement and inspection of wooden Pallets

This paper presents a comprehensive study of the application of artificial intelligence technologies to address applied challenges in modern mechanical engineering, with a focus on the development of a prototype automatic visual inspection system. Based on an analysis of current scientific and industry literature, an analytical review of key areas of AI implementation in mechanical engineering was conducted. It was established that AI has evolved from an automation tool to a key component of intelligent manufacturing systems, capable of solving cognitive problems and enabling the transition to predictive and adaptive control models. A systematic classification of mechanical engineering problems solved using AI was developed and presented, structured by stages of the product lifecycle (PLM). This clearly demonstrates the broad impact of this technology — from generative design and predictive maintenance to logistics optimization and service analytics. A detailed analysis of existing commercial solutions and successful implementation cases was conducted. It was revealed that predictive maintenance (PdM) and automatic visual inspection (AVI) systems based on computer vision are the most mature and cost-effective. A comparison of platforms from leading vendors (Siemens, Autodesk, Dassault Systèmes) showed a trend toward deep integration of AI into unified PLM ecosystems, creating digital, continuous data flows.

Automatic visual inspection has become an important application of pattern recognition, as it supports the human in this demanding and often dangerous work. Nevertheless, often missing abnormal or defective samples prohibit a supervised learning of defect models. For this reason, techniques known as one-class classification and novelty- or unusual event detection have arisen in the past years. This paper presents a new strategy to employ Hidden Markov models for defect localization in wire ropes. It is shown, that the Viterbi scores can be used as indicator for unusual subsequences. This prevents a partition of the signal into sufficient small signal windows at cost of the temporal context. Our results outperform recent time-invariant one-class classification approaches and depict a great advance for an automatic visual inspection of wire ropes.

画像処理を用いた研磨作業における外観検査の自動化 Neural Networkを用いた良否判定