Surface Inspection System for Cold Rolled Strips Based on Image ProcessingTechnique

A new surface inspection system for cold rolled strips based on image processing is introduced. The system is equipped withtwo different illumination structures and CCD matrix cameras. The structure and image processing of the inspection system are described. Some efficient algorithms for image processing and classification are presented. The system is tested with strip samples fromcold rolling plants. The results show that the system can detect and recognize six common defects of cold rolled strips successfully.

Automated deep learning system for power line inspection image analysis and processing: architecture and design issues

The continuous growth in the scale of unmanned aerial vehicle (UAV) applications in transmission line inspection has resulted in a corresponding increase in the demand for UAV inspection image processing. Owing to its excellent performance in computer vision, deep learning has been applied to UAV inspection image processing tasks such as power line identification and insulator defect detection. Despite their excellent performance, electric power UAV inspection image processing models based on deep learning face several problems such as a small application scope, the need for constant retraining and optimization, and high R&D monetary and time costs due to the black-box and scene data-driven characteristics of deep learning. In this study, an automated deep learning system for electric power UAV inspection image analysis and processing is proposed as a solution to the aforementioned problems. This system design is based on the three critical design principles of generalizability, extensibility, and automation. Pre-trained models, fine-tuning (downstream task adaptation), and automated machine learning, which are closely related to these design principles, are reviewed. In addition, an automated deep learning system architecture for electric power UAV inspection image analysis and processing is presented. A prototype system was constructed and experiments were conducted on the two electric power UAV inspection image analysis and processing tasks of insulator self-detonation and bird nest recognition. The models constructed using the prototype system achieved 91.36% and 86.13% mAP for insulator self-detonation and bird nest recognition, respectively. This demonstrates that the system design concept is reasonable and the system architecture feasible .

Image Quality Improvement for Underwater Visual Inspections of Nuclear Power Plants

Visual inspection of the key components of nuclear power plants(NPPs)is important for NPP operation and maintenance. However,the underwater environment and existing radiation will lead to image degradation,thus making it difficult to identify surface defects. In this study,a method for improving the quality of underwater images is proposed.By analyzing the degradation characteristics of underwater detection image,the image enhancement technology is used to improve the color richness of the image,and then the improved dark channel prior(DCP)algorithm is used to restore it. By modifying the estimation formula of transmittance and background light,the correction of insufficient brightness in DCP restored image is realized. The proposed method is compared with other state-of-the-art methods. The results show that the proposed method can achieve higher scores and improve the image quality by correcting the color and restoring local details,thus effectively enhancing the reliability of visual inspection of NPPs.

Study of ultrasonic phased array inspection imaging technology for NDT

A research about the ultrasonic phased array imaging principle from A-scan signal to B-scan image for non-destructive testing （NDT） was conducted in this paper, the ultrasonic phased array inspection imaging system used in industrial field was developed and the experiment was performed on the steel testing block by the system with 64 elements, 5 MHz phased array transducer. Experimental results show that the flaws could be accurately detected and the flaws size could be estimated from the B-scan images, and the B-scan images could clearly show the location of the flaws, but the quality of B-scan images needs to be enhanced by digital signal processing and controlling dynamic focusing for improving the image resolution.

Capacitive Imaging Technique for the Inspection of Composite Sucker Rod

Composite sucker rod has been extensively used due to its high strength, light weight and corrosion resistive nature. However, such composite sucker rod is diffcult for conventional non-destructive evaluation(NDE) techniques to inspect because of its complex material and/or structure. It is thus useful to embark research on developing novel NDE technique to comply the inspection requirement. This work demonstrates the feasibility of using the capacitive imaging(CI) technique for the inspection of composite sucker rod. Finite element(FE) models were constructed in COMSOL to simulate the detection of defects in the glass-fiber layer and on the carbon core surface. An FE Model based inversion method is proposed to obtain the profile of the carbon core. Preliminary CI experimental results are then presented, including the detection of surface wearing defect in the glass-fiber layer, and obtaining the profile of the carbon core. A set of accelerated aging experiments were also carried out and the results indicate that the CI technique is potentially useful in evaluating the ageing status of such composite sucker rod. The CI technique described in this work shows great potential to target some challenging tasks faced in the non-destructive evaluation of composite sucker rod, including quality control, defect detection and ageing assessment.

A Systematic Review of Computer Vision Techniques for Quality Control in End-of-Line Visual Inspection of Antenna Parts

The rapid evolution of wireless communication technologies has underscored the critical role of antennas in ensuring seamless connectivity.Antenna defects,ranging from manufacturing imperfections to environmental wear,pose significant challenges to the reliability and performance of communication systems.This review paper navigates the landscape of antenna defect detection,emphasizing the need for a nuanced understanding of various defect types and the associated challenges in visual detection.This review paper serves as a valuable resource for researchers,engineers,and practitioners engaged in the design and maintenance of communication systems.The insights presented here pave the way for enhanced reliability in antenna systems through targeted defect detection measures.In this study,a comprehensive literature analysis on computer vision algorithms that are employed in end-of-line visual inspection of antenna parts is presented.The PRISMA principles will be followed throughout the review,and its goals are to provide a summary of recent research,identify relevant computer vision techniques,and evaluate how effective these techniques are in discovering defects during inspections.It contains articles from scholarly journals as well as papers presented at conferences up until June 2023.This research utilized search phrases that were relevant,and papers were chosen based on whether or not they met certain inclusion and exclusion criteria.In this study,several different computer vision approaches,such as feature extraction and defect classification,are broken down and analyzed.Additionally,their applicability and performance are discussed.The review highlights the significance of utilizing a wide variety of datasets and measurement criteria.The findings of this study add to the existing body of knowledge and point researchers in the direction of promising new areas of investigation,such as real-time inspection systems and multispectral imaging.This review,on its whole,offers a complete study of computer vision approaches for quality control in antenna parts.It does so by providing helpful insights and drawing attention to areas that require additional exploration.

Rapid and Non-destructive Prediction of Protein Content in Peanut Varieties Using Near-infrared Hyperspectral Imaging Method

This study was undertaken to investigate the feasibility of near-infrared(NIR) hyperspectral imaging(1 000–2 500 nm) for non-destructive and quantitative prediction of protein content in peanut kernels. Partial least squares regression(PLSR) calibration model was established between the spectral data extracted from the hyperspectral images and the reference measured protein content values, with the coefficient of determination of prediction(R_P^2) of 0.885 and root mean square error of prediction(RMSEP) of 0.465%.Regression coefficients(RC) from PLSR analysis were used to identify the most essential wavelengths that had the greatest influence on changes in the protein content. Eight optimal wavelengths were selected by RC and its corresponding simplified RC-PLSR prediction model was also obtained, showing better performance with a higher R_P^2 of 0.870 and a lower RMSEP of 0.494%. The results indicate that hyperspectral imaging with PLSR analysis can be used as a rapid and non-destructive method for predicting protein content in peanut.

Application of composite field imaging in strip surface quality inspection

In strip surface quality inspection systems based on the machine vision detection technology ,image quality is a key factor affecting the final detection performance. Composite imaging methods, such as bright and dark field imaging or reflection and transmission imaging, can reveal more information by emphasizing different image aspects. Defect detection rates and defect recognition accuracy can be improved by integrating and matching information from different image acquisition settings. Practical application shows that transmission and reflection composite imaging can improve the imaging quality of penetrative defects, while bright and dark field composite imaging can enhance imaging of defects such as color deviation and stains.

Non-destructive study of fruits using grating-based X-ray imaging

Grating-based X-ray imaging can make use of conventional tube sources to provide absorption, refraction and scattering contrast images from a single set of projection images efficiently. In this paper, a fresh cherry tomato and a dried umeboshi are imaged by using X-ray Talbot–Lau interferometer. The seed distribution in the scattering image of the cherry tomato, and the wrinkles of epicarp in the refraction image of the umeboshi, are shown distinctly. The refraction and scattering images provide more information on subtle features than the absorption image. Also, the contrast-to-noise ratio values show distinguishing capacity of the three kinds of imaging techniques. The results confirm that grating-based X-ray imaging is of great potential in non-destructive fruit testing.

DEVELOPMENT AND PRELIMINARY APPLICATION OF OBJECTIFYING SYSTEM FOR TCM COLOR INSPECTION

An objectifying system for color inspections of traditional Chinese medicine （CITCM） is developed. The entire system includes two parts ： The hardware and the software. The hardware is an image acquiring device under a standard lighting condition, and it mainly includes a xenon lamp with color temperature of 5 500 K as light source, an integrating sphere used for diffusing light and a high resolution CCD camera. The software is used for digital image processing, and the procedure is divided into three steps. Firstly the skin/non-skin classifi- cation is performed by utilizing the threshold in chrominance channels of the RGB color space. Secondly, the fa- cial features are localized by using the image segmentation and coordinates sorting. Finally, the facial special re- gion（SR） corresponding to five internal organs is achieved by utilizing masks designed to take advantage of mor- phology. Subsequently, the chromaticity is calculated. The system is tested by taking 83 samples of 30 young and 53 elderly people. The experiment shows that there is significant difference of all SRs between the young and the elderly, and the system has better performance for objectifying research of CITCM.

基于ImageJ图像处理技术检测家蚕一代杂交种的良卵数和良卵率

家蚕一代杂交种的良卵数、良卵率是蚕种品质检验的2项重要指标。为了提高检验效率,利用ImageJ图像处理软件建立检测家蚕一代杂交种良卵数、良卵率的新方法。分别利用这种图像处理方法和人工计数方法检测12个家蚕品种的蚕卵样本每克蚕卵的良卵数、总卵数,采用新方法检测获得的结果数据的相对误差均<1%。对2种方法获取的每克蚕卵良卵数、总卵数数据进行t测验,其t值均小于t0.05(22)=2.074,说明2种检测方法获取的检测结果差异不显著。相关性分析表明,利用2种检测方法对每克蚕卵良卵数、总卵数的检测结果的相关系数分别为0.9979、0.9982,2种检测方法获得的结果数据间呈极显著的线性相关。与人工计数检测方法相比,采用ImageJ图像处理技术的检测方法具有方便快捷的特点,可用于生产上家蚕一代杂交种的良卵数、良卵率检测。

Machine Vision Analysis of Characteristics and Image Information Base Construction for Hybrid Rice Seed

Incompletely closed glumes, germination on panicle and disease are three important factors causing poor seed quality of hybrid rice. To determine how many and which categories should be classified to meet the demand for seed in rice production, the effects of various degrees of incompletely closed glumes, germination on panicle and disease on germination percentage at the harvest and after storage for six months were studied by standard germination percentage test. Six categories of seeds with germ (germinated seeds), severe disease, incompletely closed glumes, spot disease, fine fissure and normal seeds were inspected and then treated separately. Images of the five hybrid rice seed (Jinyou 402, Shanyou 10, Zhongyou 27, Jiayou 99 and Ⅱ you 3207) were acquired with a self-developed machine vision system. Each image could be processed to get the feature values of seed region such as length, width, ratio of length to width, area, solidity and hue. Then all the images of normal seeds were calculated to draw the feature value ranges of each hybrid rice variety. Finally, an image information base that stores typical images and related feature values of each variety was established. This image information base can help us to identify the classification limit of characteristics, and provide the reference of the threshold selection. The management of large numbers of pictures and the addition of new varieties have been supported. The research laid a foundation for extracting image features of hybrid rice seed, which is a key approach to future quality inspection with machine vision.

Application of Digital Image Analysis Method in Metric Screw Thread Metrology

This paper describes the use of computer-aided measurement for external metric screw threads. Thread parameters, including thread pitch, thread angle, pitch diameter and major diameter, were measured with CCD cameras and image analysis software. New technologies such as digital image processing were used to increase the efficiency of measurements. In this study, by reconstructing the toolmaker’s microscope, the computer-aided semi-automated measuring system was developed, which could evaluate the accuracy of screw thread profile. It is concluded that the measurement accuracy is comparable to that of traditional toolmaker’s microscope method. Key words screw threads - quality inspection - accuracy - digital image processing

Realtime Vision-Based Surface Defect Inspection of Steel Balls

In the proposed system for online inspection of steel balls, a diffuse illumination is developed to enhance defect appearances and produce high quality images. To fully view the entire sphere, a novel unfolding method is put forward based on geometrical analysis, which only requires one-dimensional movement of the balls and a pair of cameras to capture images from different directions. Moreover, a realtime inspection algorithm is customized to improve both accuracy and efficiency. The precision and recall of the sample set were 87.7% and 98%, respectively. The average time cost on image processing and analysis for a steel ball was 47 ms, and the total time cost was less than 200 ms plus the cost of image acquisition and balls' movement. The system can sort 18 000 balls per hour with a spatial resolution higher than 0.01 mm.

Laser ultrasonic testing for near-surface defects inspection of 316L stainless steel fabricated by laser powder bed fusion

The laser powder bed fusion(L-PBF)method of additive manufacturing(AM)is increasingly used in various industrial manufacturing fields due to its high material utilization and design freedom of parts.However,the parts produced by L-PBF usually contain such defects as crack and porosity because of the technological characteristics of L-PBF,which affect the quality of the product.Laser ultrasonic testing(LUT)is a potential technology for on-line testing of the L-PBF process.It is a non-contact and non-destructive approach based on signals from abundant waveforms with a wide frequency-band.In this study,a method of LUT for on-line inspection of L-PBF process was proposed,and a system of LUT was established approaching the actual environment of on-line detection to evaluate the method applicability for defects detection of L-PBF parts.The detection results of near-surface defects in L-PBF 316L stainless steel parts show that the crack-type defects with a sub-millimeter level within 0.5 mm depth can be identified,and accordingly,the positions and dimensions information can be acquired.The results were verified by X-ray computed tomography,which indicates that the present method exhibits great potential for on-line inspection of AM processes.

Damage detection with image processing: a comparative study

Large structures,such as bridges,highways,etc.,need to be inspected to evaluate their actual physical and functional condition,to predict future conditions,and to help decision makers allocating maintenance and rehabilitation resources.The assessment of civil infrastructure condition is carried out through information obtained by inspection and/or monitoring operations.Traditional techniques in structural health monitoring(SHM)involve visual inspection related to inspection standards that can be time-consuming data collection,expensive,labor intensive,and dangerous.To address these limitations,machine vision-based inspection procedures have increasingly been investigated within the research community.In this context,this paper proposes and compares four different computer vision procedures to identify damage by image processing:Otsu method thresholding,Markov random fields segmentation,RGB color detection technique,and K-means clustering algorithm.The first method is based on segmentation by thresholding that returns a binary image from a grayscale image.The Markov random fields technique uses a probabilistic approach to assign labels to model the spatial dependencies in image pixels.The RGB technique uses color detection to evaluate the defect extensions.Finally,K-means algorithm is based on Euclidean distance for clustering of the images.The benefits and limitations of each technique are discussed,and the challenges of using the techniques are highlighted.To show the effectiveness of the described techniques in damage detection of civil infrastructures,a case study is presented.Results show that various types of corrosion and cracks can be detected by image processing techniques making the proposed techniques a suitable tool for the prediction of the damage evolution in civil infrastructures.

Development of an automatic post-weld inspection system based on laser vision

In order to overcome the limitations of manual post-weld visual inspection approach, an automated inspection system is developed which uses three-dimensioual laser vision system based on the principle of optical triangulation. The system hardware consists of a modular development kit （MDK）, a computer, an actuating mechanism and so on. In image processing algorithms, extraction accuracy of centric line of laser stripe is the critical factor that determines the system performance. So according to the features of laser stripe image, a novel algorithm is developed to detect the central line of laser stripe fast and accurately. Experiments have demonstrated that this system can be used in various weld features inspection of both butt and fillet types of weld. Compared with traditional manual inspection method, this method has obvious dominance. The three-dimensional reconstruction result shows that this system has high accuracy and reliability.

Common Type of Damages in Composites and Their Inspections

This paper is a short introduction to the common type of damages in composite structures and different ways of their inspection. Due to the high increase of interest in composite materials in past decades and their usage in different structures, there is a need to discuss the damage types in them along with different ways of inspection. This paper provides a short review of these facts in order to fill out the gap that there is in the literature. Major emphasis is placed on the damage types and their mechanisms and inspection methods, mostly focused on wave propagation based structural health monitoring (SHM).

Rail fastener defect inspection method for multi railways based on machine vision

Purpose–This research aims to improve the performance of rail fastener defect inspection method for multi railways,to effectively ensure the safety of railway operation.Design/methodology/approach–Firstly,a fastener region location method based on online learning strategy was proposed,which can locate fastener regions according to the prior knowledge of track image and template matching method.Online learning strategy is used to update the template library dynamically,so that the method not only can locate fastener regions in the track images of multi railways,but also can automatically collect and annotate fastener samples.Secondly,a fastener defect recognition method based on deep convolutional neural network was proposed.The structure of recognition network was designed according to the smaller size and the relatively single content of the fastener region.The data augmentation method based on the sample random sorting strategy is adopted to reduce the impact of the imbalance of sample size on recognition performance.Findings–Test verification of the proposed method is conducted based on the rail fastener datasets of multi railways.Specifically,fastener location module has achieved an average detection rate of 99.36%,and fastener defect recognition module has achieved an average precision of 96.82%.Originality/value–The proposed method can accurately locate fastener regions and identify fastener defect in the track images of different railways,which has high reliability and strong adaptability to multi railways.

Weld seam recognition algorithm based on HSI space for pipeline ultrasonic flaw inspection

In order to realize automatic weld seam tracking for pipeline ultrasonic flaw inspection, an image processing algorithm based on HSI color space was presented. Since the color tones of weld seam were different from the parent metal, weld seam images were transformed to HSI color space. In the HSl colar space, the weld seam and base metal area can be apparently distinguished. By using this image processing algorithm, the edges and centerline of pipeline weld seam can be correctly extracted. An industrial application system was developed based on the image processing algorithm, and the image processing time is less than 70 ms and the accuracy of weld seam recognition is better than 2mm.