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Character recognizers have generally focused on printed text on paper with an emphasis on generality rather than speed. This makes the proposed algorithms not applicable in the context of the very high-speed industrial validation of expiration codes printed on the metal surface of a can. The extreme demands of speed and the adverse effects of lighting and movement, among other things, make it necessary to develop an original and specific strategy. The strategy presented in this paper first selects which of the segmented shapes of a printed can are the best candidates for comparison with expected characters. This is followed by a technique based on the comparison of templates (templates matching), which we call “morphologies”, and are represented as bitmaps to take advantage of the hardware capabilities of general-purpose processors. The use of templates has the advantage of avoiding the construction of a feature vector. In an acquisition test in a real industrial plant, we have been able to successfully treat 438 cans in 44 s, with only one validation error in one character, achieving a compromise between speed and quality that is sufficient for industrial validation in the conditions cited.

Three conveyor belts were made to examine the effect of (1) the visual arrangement, (2) working singly or in pairs, on a simulated industrial inspection task. Ninety-six subjects were given three 12-minute test sessions, one at each conveyor, inspecting plastic discs for surfaco flaws. The test material was designed so that it could be sorted and counted automatically by an electronic mechanism. Two types of error, faulty discs missed and perfect discs incorrectly selected as faulty, were analysed. In general a subject worked with the greatest accuracy when selecting faulty material from three lines of a 6-line conveyor, sitting opposite to another subject doing the same thing. Where they worked singly, performance on the 3-line conveyor was significantly more accurate than on the single line. The performance of subjects was influenced by the method of presentation of the material to be inspected and by working singly or in pairs. [ABSTRACT FROM PUBLISHER]

We present an algorithm based on maximum-likelihood analysis for the automated recognition of objects, and estimation of their pose, from 3D point clouds. Surfaces segmented from depth images are used as the features, unlike ‘interest point’-based algorithms which normally discard such data. Compared to the 6D Hough transform, it has negligible memory requirements, and is computationally efficient compared to iterative closest point algorithms. The same method is applicable to both the initial recognition/pose estimation problem as well as subsequent pose refinement through appropriate choice of the dispersion of the probability density functions. This single unified approach therefore avoids the usual requirement for different algorithms for these two tasks. In addition to the theoretical description, a simple 2 degrees of freedom (d.f.) example is given, followed by a full 6 d.f. analysis of 3D point cloud data from a cluttered scene acquired by a projected fringe-based scanner, which demonstrated an RMS alignment error as low as 0.3 mm.

Crawling robots are the focus of intelligent inspection research, and the main feature of this type of robot is the flexibility of in-plane attitude adjustment. The crawling robot HIT_Spibot is a new type of steam generator heat transfer tube inspection robot with a unique mobility capability different from traditional quadrupedal robots. This paper introduces a hierarchical motion planning approach for HIT_Spibot, aiming to achieve efficient and agile maneuverability. The proposed method integrates three distinct planners to handle complex motion tasks: a nonlinear optimization-based base motion planner, a TOPSIS-based base orientation planner, and a Mask-D3QN (MD3QN) algorithm-based gait motion planner. Initially, the robot's base and foot workspace were delineated through envelope analysis, followed by trajectory computation using Larangian methods. Subsequently, the TOPSIS algorithm was employed to establish an evaluation framework conducive to foundational turning planning. Finally, the MD3QN algorithm trained foot-points to facilitate robot movement along predefined paths. Experimental results demonstrated the method's adaptability across diverse tube structures, showcasing robust performance even in environments with random obstacles. Compared to the D3QN algorithm, MD3QN achieved a 100% success rate, enhanced average overall scores by 6.27%, reduced average stride lengths by 39.04%, and attained a stability rate of 58.02%. These results not only validate the effectiveness and practicality of the method but also showcase the significant potential of HIT_Spibot in the field of industrial inspection. [ABSTRACT FROM AUTHOR]

The increasing demand for accurate and detailed 3D modeling in fields such as cultural heritage preservation, industrial inspection, and scientific research necessitates advanced techniques to enhance model quality. This paper addresses this necessity by incorporating spectral imaging data to improve the surface detail and reflectivity of 3D models. The methodology integrates spectral imaging with traditional 3D modeling processes, offering a novel approach to capturing fine textures and subtle surface variations. The experimental results of this paper underscore the advantages of incorporating spectral imaging data in the creation of 3D models, particularly in terms of enhancing surface detail and reflectivity. The achieved experimental results demonstrate that 3D models generated with spectral imaging data exhibit significant improvements in surface detail and accuracy, particularly for objects with intricate surface patterns. These findings highlight the potential of spectral imaging in enhancing 3D model quality. This approach offers significant advancements in 3D modeling, contributing to more precise and reliable representations of complex surfaces. [ABSTRACT FROM AUTHOR]

This paper presents a measurement method that utilizes object recognition technology for continuous and quantitative real‐time monitoring of water levels in industrial boilers. Real‐time videos of water levels were monitored using a small camera, and the YOLO algorithm, a single‐stage detector, was employed to use the bounding boxes of detected objects within the video as variables, directly measuring the length ratio for each frame. The method demonstrated a high level of accuracy in water‐level measurement, with an average of 99.02%, and a stable performance, with a fluctuation of 0.13% in continuous measurements. Consequently, the proposed measurement method proves feasible for quantifying continuous water levels in industrial inspection systems even in low‐resource environments. These results demonstrate a new mechanism for monitoring technology, without requiring text detection, showing the potential for improving efficiency in complex boiler systems and the feasibility of reliable water‐level measurement and control. [ABSTRACT FROM AUTHOR]

A binocular stereo vision measurement system is widely used in fields such as industrial inspection and marine engineering due to its high accuracy, low cost, and ease of deployment. An unreasonable structural design can lead to difficulties in image matching and inaccuracies in depth computation during subsequent processing, thereby limiting the system's performance and applicability. This paper establishes a systemic error analysis model to enable the validation of changes in structural parameters on the performance of the binocular vision measurement. Specifically, the impact of structural parameters such as baseline distance and object distance on measurement error is analyzed. Extensive experiments reveal that when the ratio of baseline length to object distance is between 1 and 1.5, and the angle between the baseline and the optical axis is between 30 and 40 degrees, the system measurement error is minimized. The experimental conclusions provide guidance for subsequent measurement system research and parameter design. [ABSTRACT FROM AUTHOR]

Copyright of Experimental Technology & Management is the property of Experimental Technology & Management Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In the field of industrial inspection, accurate detection of thread quality is crucial for ensuring mechanical performance. Existing machine-vision-based methods for internal thread defect detection often face challenges in efficient detection and sufficient model training samples due to the influence of mechanical geometric features. This paper introduces a novel image acquisition structure, proposes a data augmentation algorithm based on Generative Adversarial Networks (GANs) to effectively construct high-quality training sets, and employs a YOLO algorithm to achieve internal thread defect detection. Through multi-metric evaluation and comparison with external threads, high-similarity internal thread image generation is achieved. The detection accuracy for internal and external threads reached 94.27% and 93.92%, respectively, effectively detecting internal thread defects. [ABSTRACT FROM AUTHOR]

With the development of robotics and vision measurement technology, the use of robots with line laser scanners for 3D scanning and measurement of parts has become a mainstream trend in the field of industrial inspection. Traditional scanning and measuring robots mainly use the teach-in scanning method, which has unstable scanning quality and low scanning efficiency. In this paper, the adaptive sampling method for a free-form surface, which can realize the adaptive distribution of surface measurement points according to the curvature features of free-form surfaces, is proposed first. Then, integrated with the proposed adaptive sampling method, the automatic path planning method is proposed. This method consists of adaptive sampling, scanning attitude calculation based on a quaternion, scanning viewpoint planning based on viewable cones, and scan path generation based on bi-directional scanning. Based on the proposed automatic path planning method, the scanning and measuring robot can obtain complete 3D information of the surface to be measured with high measurement accuracy and efficiency. The performance index of the laser scanner can be fully reached. [ABSTRACT FROM AUTHOR]

Copyright of Experimental Technology & Management is the property of Experimental Technology & Management Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The parameters of the ultrasonic phased array transducer are composed of frequency, array element width, array element spacing, and number of array elements, and their reasonableness is crucial for improving the detection performance of small defects, by optimizing its key parameters to complete the precise regulation to achieve more efficient acoustic field focusing and steering ability, thus optimizing the application of industrial nondestructive testing (NDT). The study firstly derives a mathematical model of focusing and steering of multi-element sound field based on the unit far-field sound pressure equation; numerically analyzes the effects of different elemental parameters on the focusing and steering performance of the PAUT by using MATLAB software; and simulates the optimized parameter conditions by combining with the COMSOL Multiphysics software, to demonstrate the improvement effect on the detection performance of small defects; finally, the optimized parameters are verified by experimental methods. The practical effect of the parameter optimization is verified by experimental methods, and the experimental results confirm the theoretical predictions, show substantial improvement in defect detection, and further strengthen the application value of the theoretical model. In this study, a parameter optimization method based on the combination of theory and multiple simulation software is proposed for the first time, which clearly elaborates the challenges of small defect detection by optimizing the transducer parameters in practical engineering applications, and deepens the theoretical basis of phased array ultrasound (PAUT) technology in industrial inspection, emphasizes the key role of parameter optimization in PAUT applications, and provides a strong theoretical and experimental support. [ABSTRACT FROM AUTHOR]

Miniature spectrometer is powerful tool for scientific research and industrial inspection. Here, we report the fabrication of graded perovskite filters with tunable bandgap and their application in constructing miniature spectrometer. The graded perovskite filters were fabricated using a Finkelstein reaction between in-situ formed halogen ion with a preformed MAPbX3 film. The graded bandgap of perovskite films can be well tunned from 400 to 750 nm by controlling the volume ratio between 5,5-dimethyl-1-pyrroline N-oxide and benzyl chloride(bromide). By combining a deep residual network, graded bandgap perovskite film and commercial CMOS sensor chip, a miniature spectrometer is demonstrated, achieving an accurate spectrum reconstruction (PSNR = 40.749) with monochromatic spectral resolution of 1.31 nm. [ABSTRACT FROM AUTHOR]

On-line non-contact surface inspection with high precision is still an open problem. Laser triangulation techniques are the most common solution for this kind of systems, but there exist fundamental limitations to their applicability when high precisions, long standoffs or large apertures are needed, and when there are difficult operating conditions. Other methods are, in general, not applicable in hostile environments or inadequate for on-line measurement. In this paper we review the latest research in Conoscopic Holography, an interferometric technique that has been applied successfully in this kind of applications, ranging from submicrometric roughness measurements, to long standoff sensors for surface defect detection in steel at high temperatures.

Nondestructive and contactless online approaches for detecting defects in polymer films are of significant interest in manufacturing. This paper develops vision-based quality metrics for detecting the defects of width consistency, film edge straightness, and specks in a polymeric film production process. The three metrics are calculated from an online low-cost grayscale camera positioned over the moving film before the final collection roller and can be implemented in real-time to monitor the film manufacturing for process and quality control. The objective metrics are calibrated to correlate with an expert ranking of test samples, and results show that they can be used to detect defects and measure the quality of polymer films with satisfactory accuracy. [ABSTRACT FROM AUTHOR]

Recent research topics have been placed on reinforcing security and safety measures within high-risk industries to protect both equipment and the environment. Numerous industries carry substantial implications for their surroundings. During significant incidents like chemical spills, or nuclear accidents, swiftly gathering precise and dynamic data poses a considerable challenge. Subsequently, this paper focuses on optimizing a mission involving multiple mobile robots charged with inspecting an industrial zone. The aim of this research is to efficiently collect measurements from diverse positions using a fleet of sensing robots operating from a central depot, that is, developing an algorithm for robot decision-making that optimizes mission planning by minimizing an objective function. Initially, we explore our previous proposed solutions and we improve the system by integrating a navigational layer to manage collision avoidance between robots. Then, we delve into scenarios involving multiple homogeneous tasks distributed in a limited geographical environment. To demonstrate feasibility, extensive simulations, numerical experiments, and comparative analysis are conducted, showing the efficiency of the proposed approaches in terms of solution quality and computational complexity. [ABSTRACT FROM AUTHOR]

The use of machine vision and deep learning for intelligent industrial inspection has become increasingly important in automating the production processes. Despite the fact that machine vision approaches are used for industrial inspection, deep learning-based defect segmentation has not been widely studied. While state-of-the-art segmentation methods are often tuned for a specific purpose, extending them to unknown sets or other datasets, such as defect segmentation datasets, require further analysis. In addition, recent contributions and improvements in image segmentation methods have not been extensively investigated for defect segmentation. To address these problems, we conducted a comparative experimental study on several recent state-of-the-art deep learning-based segmentation methods for steel surface defect segmentation and evaluated them on the basis of segmentation performance, processing time, and computational complexity using two public datasets, NEU-Seg and Severstal Steel Defect Detection (SSDD). In addition we proposed and trained a hybrid transformer-based encoder with CNN-based decoder head and achieved state-of-the-art results, a Dice score of 95.22% (NEU-Seg) and 95.55% (SSDD). [ABSTRACT FROM AUTHOR]

Periodical defect detection is a task of great importance during the production of web materials.It can reduce the appearance of a large number of surface defects, which is of vital importance to keep the product quality. In this article, a system used to detect these defects is optimized. This is carried out by looking for the optimal values for each of its configuration parameters. Since the search space formed by these parameters is very large, it cannot be explored exhaustively. For this reason, an intelligent search, like genetic algorithms, must be used. Because the fitness function is computationally heavy, a single computer would take a long time to provide an acceptable solution. For this reason, a cluster of computers is used instead, running a parallel genetic algorithm. Thus, the optimal configuration could be determined in only a few hours. [ABSTRACT FROM PUBLISHER]