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Defects in concrete surfaces are inevitably recurring during construction, which needs to be checked and accepted during construction and completion. Traditional manual inspection of surface defects requires inspectors to judge, evaluate and make decisions, which requires sufficient experience and is time-consuming and labor-intensive, and the expertise cannot be effectively preserved and transferred. In addition, the evaluation standards of different inspectors are not identical, which may lead to cause discrepancies in inspection results. Although computer vision can achieve defect recognition, there is a gap between the low-level semantics acquired by computer vision and the high-level semantics that humans understand from images. Therefore, computer vision and ontology are combined to achieve intelligent evaluation and decision-making and to bridge the above gap.

Combining ontology and computer vision, this paper establishes an evaluation and decision-making framework for concrete surface quality. By establishing concrete surface quality ontology model and defect identification quantification model, ontology reasoning technology is used to realize concrete surface quality evaluation and decision-making.

Computer vision can identify and quantify defects, obtain low-level image semantics, and ontology can structurally express expert knowledge in the field of defects. This proposed framework can automatically identify and quantify defects, and infer the causes, responsibility, severity and repair methods of defects. Through case analysis of various scenarios, the proposed evaluation and decision-making framework is feasible.

This paper establishes an evaluation and decision-making framework for concrete surface quality, so as to improve the standardization and intelligence of surface defect inspection and potentially provide reusable knowledge for inspecting concrete surface quality. The research results in this paper can be used to detect the concrete surface quality, reduce the subjectivity of evaluation and improve the inspection efficiency. In addition, the proposed framework enriches the application scenarios of ontology and computer vision, and to a certain extent bridges the gap between the image features extracted by computer vision and the information that people obtain from images.

Experimental research was conducted on the effects of surface roughness on ultrasonic non-destructive testing of electron beam melted (EBM) additively manufactured Ti-6Al-4V. Additive manufacturing (AM) is a developing technology with many potential benefits, but certain challenges posed by its use require further research before AM parts are viable for widespread use in the aviation industry. Possible applications of this new technology include aircraft battle damage repair (ABDR), small batch manufacturing to fill supply gaps and replacement for obsolete parts. This paper aims to assess the effectiveness of ultrasonic inspection in detecting manufactured flaws in EBM-manufactured Ti-6Al-4V. Additively manufactured EBM products have a high surface roughness in “as-manufactured” condition which is an artifact of the manufacturing process. The surface roughness is known to affect the results of ultrasonic inspections. Experimental data from this research demonstrate the ability of ultrasonic inspections to identify imbedded flaws as small as 0.51 mm at frequencies of 2.25, 5 and 10 MHz through a machined surface. Detection of flaws in higher surface roughness samples was increased at a frequency of 10 MHz opposed to both lower frequencies tested.

The approach is to incorporate ultrasonic waves to identify flaws in an additive manufactured specimen

A wave frequency of 10 MHz gave good results in finding flaws even with surface roughness present.

To the best of the authors’ knowledge, this was the first attempt that was able to identify small flaws using ultrasonic sound waves in which surface roughness was present.

Electronics manufacturing throughout the world now uses an increasing percentage of Surface Mount Technology (SMT). The compact and light‐weight surface‐mounted components offer a number of advantages to manufacturers. Unfortunately, however, these same beneficial characteristics make the quality of the product difficult to guarantee. As miniaturisation continues, the inspection problem becomes worse, and so advanced methods of inspection are required. Automatic inspection systems already exist, although an effective, inexpensive and reliable system has yet to be found. Recent work carried out within the Coherent and Electro‐Optics Research Group at Liverpool Polytechnic has looked at the feasibility of applying some of its established inspection methods to the problem of solder joint inspection. Extensive development must still take place; however, the methods employed have shown promise. The system uses structured light techniques to add height information to an image of the solder joint under inspection. In this way a 3‐D image of the joint may be built up, digitised and processed in a computer at high speed in order to determine its quality.

The paper presents a new approach to texture analysis. The need for a more formal definition of the term surface texture is first identified, and an appropriate texture taxonomy proposed. A method of analysis is described, synthesising innovative elements of machine vision and computer graphics to achieve an object‐centred inspection technique, which is both robust and flexible in application. A selection of experimental results is presented in the paper.

The purpose of this paper is to propose an automatic optical inspection system for measuring the surface profile of a microlens array.

The system set‐up was constructed according to the principle of the Fizeau interferometer. After capturing the ring interference fringe images of the microlens with a camera, the diameter, profile information and optical properties were analyzed through a microlens surface profile algorithm using innovative image pre‐processing with a precision of less than 0.09 micron.

By integrating with the genetic algorithm, the XY‐Table shortest moving path of the system is calculated to achieve the purpose of high‐speed inspection and automatic microlens array surface profile measurement.

The measurement results of this system were also compared with other systems, including the atomic force microscope and stylus profiler, to verify the measurement precision and accuracy of this system.

The purpose of this paper is to develop a robot vision system for surface defect detection of 3D objects. It aims at the ill‐defined qualitative items such as stains and scratches.

A robot vision system for surface defect detection may counter: high surface reflection at some viewing angles; and no reference markers in any sensed images for matching. A filtering process is used to separate the illumination and reflection components of an image. An automatic marker‐selection process and a template‐matching method are then proposed for image registration and anomaly detection in reflection‐free images.

Tests were performed on a variety of hand‐held electronic devices such as cellular phones. Experimental results show that the proposed system can reliably avoid reflection surfaces and effectively identify small local defects on the surfaces in different viewing angles.

The results have practical implications for industrial objects with arbitrary surfaces.

Traditional visual inspection systems mainly work for two‐dimensional planar surfaces such as printed circuit boards and wafers. The proposed system can find the viewing angles with minimum surface reflection and detect small local defects under image misalignment for three‐dimensional objects.

During the production process of steel strip, some defects may appear on the surface, that is, traditional manual inspection could not meet the requirements of low-cost and high-efficiency production. The purpose of this paper is to propose a method of feature selection based on filter methods combined with hidden Bayesian classifier for improving the efficiency of defect recognition and reduce the complexity of calculation. The method can select the optimal hybrid model for realizing the accurate classification of steel strip surface defects.

A large image feature set was initially obtained based on the discrete wavelet transform feature extraction method. Three feature selection methods (including correlation-based feature selection, consistency subset evaluator [CSE] and information gain) were then used to optimize the feature space. Parameters for the feature selection methods were based on the classification accuracy results of hidden Naive Bayes (HNB) algorithm. The selected feature subset was then applied to the traditional NB classifier and leading extended NB classifiers.

The experimental results demonstrated that the HNB model combined with feature selection approaches has better classification performance than other models of defect recognition. Among the results of this study, the proposed hybrid model of CSE + HNB is the most robust and effective and of highest classification accuracy in identifying the optimal subset of the surface defect database.

The main contribution of this paper is the development of a hybrid model combining feature selection and multi-class classification algorithms for steel strip surface inspection. The proposed hybrid model is primarily robust and effective for steel strip surface inspection.

The technique of Optically Stimulated Electron Emission (OSEE) is considered as a method of inspecting printed circuit based electronics assemblies for flux residue immediately following production soldering. The technique has been used for several years by NASA and its contractors in the refurbishment of solid rocket motors for the Space Shuttle. The application to copper substrates and soldered copper substrates has shown sensitivity to small amounts of residues of some solder fluxes. The technique was extended for inspection of insulating substrates used in printed wiring board (PWB) construction by altering the measurement procedure to include charge replacement, thereby attaining measurement reproducibility. The results indicate that OSEE inspection of electronic assemblies for flux residues is feasible. An inspection based on this technology subjects the inspected object only to photons of ultra‐violet light and immersion in an inert gas, such as argon. It is potentially rapid enough to provide 100% inspection of boards processed on a production line, and it has potential spatial resolution of less than 1 micron.

The traditional methods have difficulty to inspection various types of copper strips defects as inclusions, pits and delamination defects under uneven illumination. Therefore, this paper aims to propose an optimal real Gabor filter model for inspection; however, improper selection of Gabor parameters will cause the boundary between the defect and the background image to be not very clear. This will make the defect and the background cannot be completely separated.

The authors proposed an optimal Real Gabor filter model for inspection of copper surface defects under uneven illumination. This proposed method only requires a single filter by calculating the specific convolution energy of the Gabor filter with the image. The Real Gabor filter’s parameter is optimized by particle swarm optimization (PSO), which objective fitness function is maximization of the Gabor filter’s energy average divided by the energy standard deviation, the objective makes a distinction between the defect and normal area.

The authors have verified the effect with different iterations of parameter optimization using PSO, the effects with different control constant of energy and neighborhood window size of real Gabor filter, the experimental results on a number of metal surface have shown the proposed method achieved a well performance in defect recognition of metal surface.

The authors propose a defect detection method based on particle swarm optimization for single Gabor filter parameters optimization. This proposed method only requires a single filter and finds the best parameters of the Gabor filter. By calculating the specific convolution energy of the Gabor filter and the image, to obtain the best Gabor filter parameters and to highlight the defects, the particle swarm optimization algorithm’s fitness objective function is maximize the Gabor filter's average energy divided by the energy standard deviation.