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.

Inspection system for welded tubular joint based on ultrasonic phased array

A manual inspection of large-diameter tubular joints is difficult. As a result a scanner with three degrees of freedom （ DOFs） was developed based on the scanning principle of ultrasonic phased array. The weld tracing is realized by a 2DOF motion of scanner. The pose of ultrasonic probe is controlled by the third one. The control strategy is put forward based on a programmable multi-axis controller. Four kinds of scanning modes can be implemented simultaneously employing this ultrasonic inspection system. Experiments on reference blocks of tubular joints reveal that the automatic ultrasonic phased array inspection system has the same inspection accuracy as a manual ultrasonic inspection. This system is superior to the manual ultrasonic system in terms of reliability and repeatability. The artificial dejects of weld at tubular joint can be detected accurately with the presented inspection system.

Online application of automatic surface quality inspection system to finishing line of cold rolled strips

An automatic surface quality inspection system installed on a finishing lineof cold rolled strips is introduced. The system is able to detect surface defects on cold rolledstrips, such as scratches, coil breaks, rusts, roll imprints, and so on. Multiple CCD area scancameras were equipped to capture images of strip surface simultaneously. Defects were detectedthrough 'Dark-field illumination' which is generated by LED illuminators. Parallel computationtechnique and fast processing algorithms were developed for real-time data processing. Theapplication to the production line shows that the system is able to detect defects effectively.

Application of automatic surface inspection system in cold-rolled strip production

The detection and classification of real-time surface defects play an important role in automotive sheet inspection and production. In this paper, an automatic surface inspection system （ASIS） based on signal processing in Baosteel NO. 4 cold-rolled plant is briefly presented. We demonstrate that the strip surface defect properties such as image, type, pitch, and position can be accurately calculated and classified by the automatic surface inspection system. In the manufacturing of the high-quality cold-rolled strips, it is necessary that the real-time surface defects can be detected and transferred by the automatic surface inspection system combined with annealing lines and recoiling lines.

Automated Visual Inspection System for Specifying Brick Quality

Automated visual inspection system has been developed to specify brick quality and the accepting of the bricks in a production line. This system is based on CMOS web-camera placed in manufacture line. Depending on diameters, area, perimeter and cracks of a brick, a strong algorithm has been developed, and this algorithm is created to befit the required for measuring bricks quality. The quality is measured by fuzzy system which can give percent accepting to a brick under the test. Fuzzy reasoning gives the system more reliability than other inspection system.

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.

Research on automatic inspection system for defects on precise optical surface based on machine vision

In manufacture of precise optical products, it is important to inspect and classify the potential defects existing on the products’ surfaces after precise machining in order to obtain high quality in both functionality and aesthetics. The existing methods for detecting and classifying defects all are low accuracy or efficiency or high cost in inspection process. In this paper, a new inspection system based on machine vision has been introduced, which uses automatic focusing and image mosaic technologies to rapidly acquire distinct surface image, and employs Case-Based Reasoning(CBR)method in defects classification. A modificatory fuzzy similarity algorithm in CBR has been adopted for more quick and robust need of pattern recognition in practice inspection. Experiments show that the system can inspect surface diameter of 500mm in half an hour with resolving power of 0.8μm diameter according to digs or 0.5μm transverse width according to scratches. The proposed inspection principles and methods not only have meet manufacturing requirements of precise optical products, but also have great potential applications in other fields of precise surface inspection.

Pipe Inspection System by Guide Wave Using a Long Distance Waveguide

In the industrial fields, many high temperature structures that require a non-destructive inspection exist. However, there are currently few sensors that can carry out non-destructive testing in a high temperature environment. In particular, the ultrasonic sensor is normally not used at over 50 degrees Celsius. Also, a special sensor for high temperature is currently available, but there are various constraints;it has not yet reached a level that is useful in industry. Therefore, we have been developing a new sensor system using a long waveguide which can transmit an ultrasonic wave from a long distance. Especially, this study focuses on applying the developed technique to a pipe which is used in a nuclear power plant. Therefore, the best rectangular-shaped waveguide was studied and attempted to be wound around a pipe to be driven by an acoustic source of a guide wave. Finally, the L (0, 2) and T (0, 1)-mode guide waves were successfully detected by optimizing the shape of the opposite edge of the rectangular-shaped waveguide that could detect the reflected signal from an artificial defect machined into a test pipe.

The Study of BIM-Based MRT Structural Inspection System

The Taipei MRT （Metropolitan Rapid Transit） system has been operated since 1996. Facing the challenge of safety and sustainability of MRT, it is urgent to develop a structural inspection strategy and maintenance system based on the concept of life cycle management. In the mean while, BIM （Building Information Modeling） has become an emerging technology in the architecture and construction industries. BIM utilizes the 3D （3 Dimensions） CAD objects to simulate the real word building elements and with the ability to maintain life cycle information for a building. BIM is an ideal tool and platform for developing an inspection and maintenance system. The goal of this research is to establish a MRT structural inspection system concept based on BIM and life cycle management. In this study, a BIM model of shield tunnel was established to demonstrate the integration of 3D CAD with inspection records. A MRT structural maintenance database, which includes structural deterioration types, inspection records, and repair methods, was designed to interact with the BIM. An inspection prototype system was designed and implemented to perform the MRT structural inspection. The proposed system was designed with mobile-side and server-side systems. The mobile-side system was designed to perform on mobile devices such as tablet computer or smart phones with the ability to mark defects on BIM, as well as to fill in inspection sheets and photos; the server-side system can assign inspection tasks, maintains all inspection records, automatically output inspection reports, and analyze the database to prioritize all maintenance tasks.

Application of Mobile Intelligent Inspection System in Substation Equipment Management

Intelligent inspection is more and more widely applied in all walks of life. This paper introduces the application of the intelligent inspection system in substation equipment management based on mobile phone terminal. It can make the power supply enterprise have more effective means to ensure the safe operation of equipment, improve equipment management level, and improve the economic benefits of power supply.

A New Guide Wave Inspection System Using Three Polarized Transverse Wave EMATs without Any Couplant

A guide wave is provided with the characteristic of long range propagation in the axis direction of a pipe, so it is possible to detect many defects over a large pipe area at once. At present, there is a technique to generate a guide wave using a piezoelectric element (PZT). However, the transverse wave-transducer using PZT needs to require a high viscosity couplant because the transverse wave cannot travel into typical liquid like water or oil. A guide wave inspection system that uses an electromagnetic ultrasonic transducer (EMAT) which does not require any couplant has then been developed to solve this trouble. First, a guide wave into a pipe, L, T and F-mode, can be transmitted and received by a polarized shear horizontal transverse wave propagating to the thickness direction when the vibration direction has been adjusted to the best direction. At next stage, the three EMATs for L, T and F-mode with different polarized vibration directions were piled up to improve the performance at the same position under the permanent magnet to inspect the pipe at the same condition. Next, the system with the EMATs can be confirmed to be able to detect three guide wave modes signal with enough intensity. Finally, the detection performance using the test pipes with any artificial defects has been done by the developed pipe inspection system, and any drilled holes and any notches can be detected. It is indicated that the developed system could be useful in real industrial field.

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.

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 .

Central environmental protection inspection and carbon emission reduction: A tripartite evolutionary game model from the perspective of carbon neutrality

Since the carbon neutrality target was proposed,many countries have been facing severe challenges to carbon emission reduction sustainably.This study is conducted using a tripartite evolutionary game model to explore the impact of the central environmental protection inspection(CEPI)on driving carbon emission reduction,and to study what factors influence the strategic choices of each party and how they interact with each other.The research results suggest that local governments and manufacturing enterprises would choose strategies that are beneficial to carbon reduction when CEPI increases.When the initial willingness of all parties increases 20%,50%—80%,the time spent for the whole system to achieve stability decreases from 100%,60%—30%.The evolutionary result of“thorough inspection,regulation implementation,low-carbon management”is the best strategy for the tripartite evolutionary game.Moreover,the smaller the cost and the larger the benefit,the greater the likelihood of the three-party game stability strategy appears.This study has important guiding significance for other developing countries to promote carbon emission reduction by environmental policy.

Unmanned Aerial Vehicle Inspection Routing and Scheduling for Engineering Management

Technological advancements in unmanned aerial vehicles(UAVs)have revolutionized various industries,enabling the widespread adoption of UAV-based solutions.In engineering management,UAV-based inspection has emerged as a highly efficient method for identifying hidden risks in high-risk construction environments,surpassing traditional inspection techniques.Building on this foundation,this paper delves into the optimization of UAV inspection routing and scheduling,addressing the complexity introduced by factors such as no-fly zones,monitoring-interval time windows,and multiple monitoring rounds.To tackle this challenging problem,we propose a mixed-integer linear programming(MILP)model that optimizes inspection task assignments,monitoring sequence schedules,and charging decisions.The comprehensive consideration of these factors differentiates our problem from conventional vehicle routing problem(VRP),leading to a mathematically intractable model for commercial solvers in the case of large-scale instances.To overcome this limitation,we design a tailored variable neighborhood search(VNS)metaheuristic,customizing the algorithm to efficiently solve our model.Extensive numerical experiments are conducted to validate the efficacy of our proposed algorithm,demonstrating its scalability for both large-scale and real-scale instances.Sensitivity experiments and a case study based on an actual engineering project are also conducted,providing valuable insights for engineering managers to enhance inspection work efficiency.

A Railway Fastener Inspection Method Based on Abnormal Sample Generation

Regular fastener detection is necessary to ensure the safety of railways.However,the number of abnormal fasteners is significantly lower than the number of normal fasteners in real railways.Existing supervised inspectionmethods have insufficient detection ability in cases of imbalanced samples.To solve this problem,we propose an approach based on deep convolutional neural networks(DCNNs),which consists of three stages:fastener localization,abnormal fastener sample generation based on saliency detection,and fastener state inspection.First,a lightweight YOLOv5s is designed to achieve fast and precise localization of fastener regions.Then,the foreground clip region of a fastener image is extracted by the designed fastener saliency detection network(F-SDNet),combined with data augmentation to generate a large number of abnormal fastener samples and balance the number of abnormal and normal samples.Finally,a fastener inspection model called Fastener ResNet-8 is constructed by being trained with the augmented fastener dataset.Results show the effectiveness of our proposed method in solving the problem of sample imbalance in fastener detection.Qualitative and quantitative comparisons show that the proposed F-SDNet outperforms other state-of-the-art methods in clip region extraction,reaching MAE and max F-measure of 0.0215 and 0.9635,respectively.In addition,the FPS of the fastener state inspection model reached 86.2,and the average accuracy reached 98.7%on 614 augmented fastener test sets and 99.9%on 7505 real fastener datasets.

Distributed Resource Allocation in Dispersed Computing Environment Based on UAV Track Inspection in Urban Rail Transit

With the rapid development of urban rail transit,the existing track detection has some problems such as low efficiency and insufficient detection coverage,so an intelligent and automatic track detectionmethod based onUAV is urgently needed to avoid major safety accidents.At the same time,the geographical distribution of IoT devices results in the inefficient use of the significant computing potential held by a large number of devices.As a result,the Dispersed Computing(DCOMP)architecture enables collaborative computing between devices in the Internet of Everything(IoE),promotes low-latency and efficient cross-wide applications,and meets users’growing needs for computing performance and service quality.This paper focuses on examining the resource allocation challenge within a dispersed computing environment that utilizes UAV inspection tracks.Furthermore,the system takes into account both resource constraints and computational constraints and transforms the optimization problem into an energy minimization problem with computational constraints.The Markov Decision Process(MDP)model is employed to capture the connection between the dispersed computing resource allocation strategy and the system environment.Subsequently,a method based on Double Deep Q-Network(DDQN)is introduced to derive the optimal policy.Simultaneously,an experience replay mechanism is implemented to tackle the issue of increasing dimensionality.The experimental simulations validate the efficacy of the method across various scenarios.

Automatic Road Tunnel Crack Inspection Based on Crack Area Sensing and Multiscale Semantic Segmentation

The detection of crack defects on the walls of road tunnels is a crucial step in the process of ensuring travel safetyand performing routine tunnel maintenance. The automatic and accurate detection of cracks on the surface of roadtunnels is the key to improving the maintenance efficiency of road tunnels. Machine vision technology combinedwith a deep neural network model is an effective means to realize the localization and identification of crackdefects on the surface of road tunnels.We propose a complete set of automatic inspection methods for identifyingcracks on the walls of road tunnels as a solution to the problem of difficulty in identifying cracks during manualmaintenance. First, a set of equipment applied to the real-time acquisition of high-definition images of walls inroad tunnels is designed. Images of walls in road tunnels are acquired based on the designed equipment, whereimages containing crack defects are manually identified and selected. Subsequently, the training and validationsets used to construct the crack inspection model are obtained based on the acquired images, whereas the regionscontaining cracks and the pixels of the cracks are finely labeled. After that, a crack area sensing module is designedbased on the proposed you only look once version 7 model combined with coordinate attention mechanism (CAYOLOV7) network to locate the crack regions in the road tunnel surface images. Only subimages containingcracks are acquired and sent to the multiscale semantic segmentation module for extraction of the pixels to whichthe cracks belong based on the DeepLab V3+ network. The precision and recall of the crack region localizationon the surface of a road tunnel based on our proposed method are 82.4% and 93.8%, respectively. Moreover, themean intersection over union (MIoU) and pixel accuracy (PA) values for achieving pixel-level detection accuracyare 76.84% and 78.29%, respectively. The experimental results on the dataset show that our proposed two-stagedetection method outperforms other state-of-the-art models in crack region localization and detection. Based onour proposedmethod, the images captured on the surface of a road tunnel can complete crack detection at a speed often frames/second, and the detection accuracy can reach 0.25 mm, which meets the requirements for maintenanceof an actual project. The designed CA-YOLO V7 network enables precise localization of the area to which a crackbelongs in images acquired under different environmental and lighting conditions in road tunnels. The improvedDeepLab V3+ network based on lightweighting is able to extract crack morphology in a given region more quicklywhile maintaining segmentation accuracy. The established model combines defect localization and segmentationmodels for the first time, realizing pixel-level defect localization and extraction on the surface of road tunnelsin complex environments, and is capable of determining the actual size of cracks based on the physical coordinatesystemafter camera calibration. The trainedmodelhas highaccuracy andcanbe extendedandapplied to embeddedcomputing devices for the assessment and repair of damaged areas in different types of road tunnels.

Development of track geometry inspection equipment for high-speed comprehensive inspection train in China

Purpose–This study aims to analyze the development direction of track geometry inspection equipment for high-speed comprehensive inspection train in China.Design/methodology/approach–The development of track geometry inspection equipment for highspeed comprehensive inspection train in China in the past 20 years can be divided into 3 stages.Track geometry inspection equipment 1.0 is the stage of analog signal.At the stage 1.0,the first priority is to meet the China’s railways basic needs of pre-operation joint debugging,safety assessment and daily dynamic inspection,maintenance and repair after operation.Track geometry inspection equipment 2.0 is the stage of digital signal.At the stage 2.0,it is important to improve stability and reliability of track geometry inspection equipment by upgrading the hardware sensors and improving software architecture.Track geometry inspection equipment 3.0 is the stage of lightweight.At the stage 3.0,miniaturization,low power consumption,self-running and green economy are co-developing on demand.Findings–The ability of track geometry inspection equipment for high-speed comprehensive inspection train will be expanded.The dynamic inspection of track stiffness changes will be studied under loaded and unloaded conditions in response to the track local settlement,track plate detachment and cushion plate failure.The dynamic measurement method of rail surface slope and vertical curve radius will be proposed,to reveal the changes in railway profile parameters of high-speed railways and the relationship between railway profile,track irregularity and subsidence of subgrade and bridges.The 200 m cut-off wavelength of track regularity will be researched to adapt to the operating speed of 400 km/h.Originality/value–The research can provide new connotations and requirements of track geometry inspection equipment for high-speed comprehensive inspection train in the new railway stage.

High-speed railway track components inspection framework based on YOLOv8 with high-performance model deployment

Railway inspection poses significant challenges due to the extensive use of various components in vast railway networks,especially in the case of high-speed railways.These networks demand high maintenance but offer only limited inspection windows.In response,this study focuses on developing a high-performance rail inspection system tailored for high-speed railways and railroads with constrained inspection timeframes.This system leverages the latest artificial intelligence advancements,incorporating YOLOv8 for detection.Our research introduces an efficient model inference pipeline based on a producer-consumer model,effectively utilizing parallel processing and concurrent computing to enhance performance.The deployment of this pipeline,implemented using C++,TensorRT,float16 quantization,and oneTBB,represents a significant departure from traditional sequential processing methods.The results are remarkable,showcasing a substantial increase in processing speed:from 38.93 Frames Per Second(FPS)to 281.06 FPS on a desktop system equipped with an Nvidia RTX A6000 GPU and from 19.50 FPS to 200.26 FPS on the Nvidia Jetson AGX Orin edge computing platform.This proposed framework has the potential to meet the real-time inspection requirements of high-speed railways.