Newer knowledge of nutrition

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.

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.

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).

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

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.

In this paper, we propose a Phase Fourier Reconstruction (PFR) approach for anomaly detection on metal surfaces using salient irregularities. To get salient irregularity with images captured from an automatic visual inspection (AVI) system using different lighting settings, we first trained a classifier for image selection as only dark images are utilized for anomaly detection. By doing so, surface details, part design, and boundaries between foreground/background become indistinct, but anomaly regions are highlighted because of diffuse reflection caused by rough surfaces. Then PFR is applied so that regular patterns and homogeneous regions are further de-emphasized, and simultaneously, anomaly areas are distinct and located. Different from existing phase-based methods which require substantial texture information, our PFR works on both textual and non-textual images. Unlike existing template matching methods which require prior knowledge of defect-free patterns, our PFR is an unsupervised approach which detects anomalies using a single image. Experimental results on anomaly detection clearly demonstrate the effectiveness of the proposed method which outperforms several well-designed methods [8, 12, 15, 16, 18, 19] with a running time of less than 0.01 seconds per image.

Surface-mounted technology (SMT) welding defect detection plays a key role in the printed circuit board assembly (PCBA) production process, which affects the use of electronic products and cost. Previous works tend to realize defect detection with only defect samples and they assume that there are sufficient defect samples. However, defect samples are usually difficult to collect in real-life scenarios while enough template samples can be easily obtained. In addition, most existing works carry out defect detection based on benchmarks with simple backgrounds of PCBA, which is not suitable for PCBA with complex structures in modern electronic product manufacturing. To address the above issues, we propose a template-guidance Siamese network based on YOLO for SMT welding defect detection (TGSYOLO), which is deployed on a real SMT automatic optical inspection (AOI) system. First, the two-stream structure is introduced to extract deep features in defect images and template images, in which template features serve as guidance knowledge. Then, a template fusion Transformer (TFT) is proposed to model global features between detect and template features in the low-level stage, which could acquire long-range correlations to force the network to focus on potential defect regions. Next, to avoid the disappearance of tiny defect features during deep feature fusion, a multiscale attention feature pyramid network (MAFPN) is proposed to directly fuse defect semantic information from low-level features, which retains detailed expressions of defects through skip connection and obtains compact fusion features. Furthermore, we collect limited welding defect samples based on more complex PCBA backgrounds than previous works through a real SMT AOI system. Experiments on the limited dataset show that TGSYOLO could reach 0.985 of mAP@0.5, 0.885 of mAP@0.75, and 0.984 of F1, which is 0.008, 0.054, and 0.025 higher than other SOTA methods. Also, generalization experiments on the public DeepPCB show that TGSYOLO could still reach the best with 0.991 of mAP@0.5 and 0.89 of mAP@0.75, which proves that TGSYOLO has good generalization performance.

Chapter 7 - Industrial Inspection Systems

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.

This study proposed to use wavelet transfer to acquire image hue and value as image features, and use Back-propagation Neural Network Fuzzy Clustering analysis to recognize type of textile texture. Firstly, the RGB color space of original color image is transferred to HSV color space; secondly, wavelet transfer is applied to obtain vertical, horizontal and diagonal images of hue and value, and compute its wavelet energy to take them as texture features of this image. Finally, the back-propagation neural network is adopted to make fuzzy clustering analysis of this image texture feature. As indicated by experimental result, this system can recognize accurately plain weave, twill weave and satin weave textures in woven fabric, single and double textures in knitted fabric, and nonwoven texture in nonwoven fabric. Among 300 test samples in total where there are 50 samples each kind of fabric texture, the general recognition rate amounts to 97.67%. Therefore, this study succeeded in building the automatic computer visual inspection system to recognize textile texture type, which can greatly improve and avoid current low efficiency, non-objective judgment and labor waste due to human inspection.

As defect densities decrease, ever increasing sample areas will be required for statistically valid estimates of defect densities. Several tools exist, such as electrical defect monitors (serpentines and combs), that can test a large area but provide little information about the defects detected. At the same time, automated visual inspection techniques (KLA 2028) exist that inspect smaller areas with greater detail. We propose a strategy for analyzing large areas with great detail by combining both of these tools. The necessity of obtaining visual information for process evaluations is discussed. Experimental results are described in support of this strategy.

This paper proposes a method to accurately detect anomaly from an image by combining features extracted by discriminative and generative models. Automatic anomaly detection is a key factor for reducing operation costs of visual inspection in a wide range of domains. The proposed method consists of three sub-networks. The first sub-network is convolutional neural networks as a discriminative model for extracting features to distinguish between anomaly and normal. The second subnetwork is a variational autoencoder as a generative model to extract features representing normal. The third sub-network is neural networks to discriminate between anomaly and normal on the basis of features from the discriminative and generative models. Experiments were conducted using pseudo anomalous images generated by superimposing anomaly which was manually extracted from real images. Results of the experiments show that the proposed method improves the area under the curve by 0.08-0.33 points compared with that of a conventional method. With high accuracy, automatic visual inspection systems can be implemented for reducing operation costs.

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.

Automated visual inspection (AVI) systems these days are considered essential in the assembly of surface-mounted devices (SMDs) in the electronics industry. This industry has faced the problem of rapid introduction and retirement of SMD-based products with the consequent obsolescence of the inspection systems already in the assembly lines. The constant introduction of new products has caused AVI systems to become rapidly obsolete. The general goal of this research centers on developing self-training AVI systems for the inspection of SMD components. The premise is that these systems would be less prone to obsolescence. In this paper, the authors describe the methodology being used for automatically selecting the features to inspect new components. In particular, this paper explores the use of multivariate stepwise discriminant analysis techniques, such as Wilks' Lambda, in order to automate the feature selection process. All of these techniques are applied to a case study of the inspection of SMD components. Note to Practitioners-In this paper, we present a methodology that would allow the automation of the tedious task of exploring and selecting features to develop algorithms for automated visual inspection systems. In particular, the proposed method selects a subset of features among all of the known features. The chosen subset seeks to minimize inspection errors while keeping the algorithmic development time to a minimum. This is particularly useful for adapting pre-existing 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 surface-mounted devices

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.