Automatic Inspection System Research Articles

Automated Optical Inspection (AOI) for FOPLP with Simultaneous Die Placement Metrology

Abstract As the final step of IC fabrication, packaging is the process to encapsulate the chip and provide the interconnections for the I/O of the final form factor. The demand for increasingly hig...

Conductive particle detection via deep learning for ACF bonding in TFT-LCD manufacturing

The inspection of conductive particles after Anisotropic Conductive Film (ACF) bonding is a common and crucial step in the TFT-LCD manufacturing process since the number of high-quality conductive particles is a key indicator of ACF bonding quality. However, manual inspection under microscope is a time-consuming, tedious, and error-prone. Therefore, there is an urgent demand in industry for the automatic conductive particle inspection system. It is challenging for automatic conductive particle quality inspection due to the existence of complex background noise and diversified particle appearance, including shape, size, clustering and overlapping, etc. As a result, it lacks an effective automatic detection method to handle all the complex particle patterns. In this paper, we propose a U-shaped deep residual neural network (i.e., U-ResNet), which can learn features of particle from massively labeled data. The experimental results show that the proposed method achieves high detection accuracy and recall rate, which exceedingly outperforms the previous work. Also, our system is very efficient and can work in real time.

Surface Quality Assurance Method for Lithium-Ion Battery Electrode Using Concentration Compensation and Partiality Decision Rules

This paper presents a novel method for lithium-ion battery electrode (LIBE) surface quality assurance. First, based on machine vision, an automatic optical inspection system is developed to check defects on LIBE. In addition, a background normalization algorithm is put forward to preprocess the large-scale LIBE with inhomogeneous thickness in uneven illumination. With the help of the auto-concentration compensation algorithm, flaws can be extracted precisely. Moreover, after characterizing the defects, features machine applied partiality parameter automatic adjustment method and partiality decision rules are exerted for defects accurate classification, which provides near-optimal performance and reduces the complexity of tuning parameters. The proposed method is computationally efficient and satisfies real-time online inspection requirement. Experimental results verify the effectiveness and performance of the proposed method according to the inspection speed and recognition rate.

A CNN-Based Defect Inspection Method for Catenary Split Pins in High-Speed Railway

Split pins (SPs) play an important role in fixing joint components on catenary support devices (CSDs) of high-speed railway. The occurrence of loose and missing defects of SPs could make the structure of CSDs unstable. In this paper, we present a three-stage automatic defect inspection system for SPs mainly based on an improved deep convolutional neural network (CNN), which is called PVANET++. First, SPs are localized by PVANET++ and the Hough transform & Chan–Vese model, and then, three proposed criteria are applied to detect defects of SPs. In PVANET++, a new anchor mechanism is applied to produce suitable candidate boxes for objects, and multiple hidden layer features are combined to construct discriminative hyperfeatures. The performance of PVANET++ and several recent state-of-the-art deep CNNs is compared in a data set that is collected from a 60-km rail line. The results show that our model is superior to others in accuracy, and has a considerable speed.

Rescan Strategy for Time Efficient View and Path Planning in Automated Inspection System

In 3D robotic inspection systems, view planning and path planning are important to minimize the operation time of the system. In this study, an automated inspection system is proposed that consists of a structured light scanner, a collaborative robot, and a rotary table. In addition, a rescan strategy algorithm is proposed that can minimize the operation time of the system and enhance the reliability of the inspection task. This algorithm uses a small number of viewpoints for view planning by selecting an area to be rescanned. At the same time, path planning with the rotation and alignment of viewpoints using a rotary table make it possible to reduce the scanning time. Experimental results on various objects have verified that the proposed method can be applied to the 3D quality inspection task with a reasonable operation time.

An automatic vision inspection system for detecting surface cracks of welding joint

An automatic vision inspection system for detecting surface cracks of welding joint

Automatic visual inspection system for quality control of the sandwich panel and detecting the dipping and buckling of the surfaces

In this paper, an automated inspection system is proposed for detecting the location and measuring the size of existing dipping or buckling on the sandwich panel surface using an RGB camera and an inexpensive linear laser. The proposed method, by observing the radiated laser beams on the sandwich panel surface, can localize and calculate the level of dipping and buckling with acceptable accuracy while being robust to vibrations of moving sandwich panel on the production line conveyor. After a complete processing of the panel by the system, a three-dimensional (surface plot) or two-dimensional (heat map) output is produced to assist the production line supervisor to easily inspect the surface quality of the sandwich panels. Our experimental results show that the proposed system can detect and measure the surface defects including dipping and buckling with a high accuracy and performance.

Safety Distance Identification for Crane Drivers Based on Mask R-CNN.

Tower cranes are the most commonly used large-scale equipment on construction site. Because workers can’t always pay attention to the environment at the top of the head, it is often difficult to avoid accidents when heavy objects fall. Therefore, safety construction accidents such as struck-by often occurs. In order to address crane issue, this research recorded video data by a tower crane camera, labeled the pictures, and operated image recognition with the MASK R-CNN method. Furthermore, The RGB color extraction was performed on the identified mask layer to obtain the pixel coordinates of workers and dangerous zone. At last, we used the pixel and actual distance conversion method to measure the safety distance. The contribution of this research to safety problem area is twofold: On one hand, without affecting the normal behavior of workers, an automatic collection, analysis, and early-warning system was established. On the other hand, the proposed automatic inspection system can help improve the safety operation of tower crane drivers.

Design and Build an Automatic Train Passenger Ticket Inspection Management System Using RFID

Currently, public transportation is needed by Indonesian people to meet human needs for increased movement or mobility. Along with the increasing public interest in trains demanding an increase in services such as the checking system for passenger tickets still using manual system by officers by approaching passengers in each car. This is'nt efficient, so an automatic passenger ticket inspection system using RFID is required. Passenger RFID is scanned when check-in at station then the data is sent to server via nodeMCU ESP8266, the officer only checks passengers through the data display on the monitor. This system also has a Flex sensor in each seat which functions to detect seats that aren't purchased but are occupied by other passengers.The test results concluded that the reading of the RFID card against RFID reader reached distance and barrier with a thickness of 3cm, the DHT11 temperature sensor showed reading accuracy up to 96.6%, and the voltage on the flex sensor on seat 1 had average load of 2.6V, seat 2 average 2.82V, seat 3 averaged 2.84V, seat 4 averaged 2.67V, while the voltage on the flex sensor in seat 1 had an average load of 2.38V, seat 2 averaged 2.29V , seat 3 averages 2.65V, seat 4 averages 2.60V. The value of the flex sensor tension in each condition isn't the same for each seat because the surface shape and thickness of each chair are different. All systems can run properly, starting from sensor readings, transmitting data and displaying data on web.

Computer Vision Method in Beer Quality Evaluation—A Review

Beers are differentiated mainly according to their visual appearance and their fermentation process. The main quality characteristics of beer are appearance, aroma, flavor, and mouthfeel. Important visual attributes of beer are foam appearance (volume and persistence), as well as the color and clarity. To replace manual inspection, automatic, objective, rapid and repeatable external quality inspection systems, such as computer vision, are becoming very important and necessary. Computer vision is a non-contact optical technique, suitable for the non-destructive evaluation of the food product quality. Currently, the main application of computer vision occurs in automated inspection and measurement, allowing manufacturers to keep control of product quality. This paper presents an overview of the applications and the latest achievements of the computer vision methods in determining the external quality attributes of beer.

A prediction of leaf mechanical properties with data mining

The mechanical properties of the leaf are typically determined by mechanical testing approaches to study the leaf lifespan, plant anti-herbivore defences and ecological functions by considering habitat influences, environmental resources variation and species diversity. While the leaf morphology features were commonly used for plant recognition and plant disease detection with the aid of an automated inspection system. However, the influence of morphology features on leaf mechanical properties is vague. In this research, we investigated the effect of various morphological features on mechanical properties of leaf, followed by proposed a novel leaf mechanical properties prediction model using data mining techniques. A 600 × 22 feature vector was collected and examined using Pearson correlation analysis and Welch’s test to select the relevant features. The prediction on four mechanical properties indicators was performed with LinearRegression, KStar, DecisionTable and M5P algorithms in the Waikato Environment for Knowledge Analysis (WEKA). The experimental results show that numeric prediction for Tearing Force (FT) and Tearing Strength (ST) (RRSE ≈ 25%) were about two folds better as compared to Work-to-tear (WT) and Specific Work-to-tear (SWT) (RRSE ≈ 50%) in four algorithms tested. The best results achieved was the FT indicator prediction with M5P algorithms (RRSE = 23.12%). FT indicator prediction model adopted from M5P algorithms output was constructed.

A Machine Vision Based Automatic Optical Inspection System for Detecting Defects of Rubber Keypads of Scanning Machine

A Machine Vision Based Automatic Optical Inspection System for Detecting Defects of Rubber Keypads of Scanning Machine

Intelligent Monitoring and Inspection of Power Line Components Powered by UAVs and Deep Learning

In this paper, we present a novel automatic autonomous vision-based power line inspection system that uses unmanned aerial vehicle inspection as the main inspection method, optical images as the primary data source, and deep learning as the backbone of the data analysis. To facilitate the implementation of the system, we address three main challenges of deep learning in vision-based power line inspection: (i) the lack of training data; (ii) class imbalance; and (iii) the detection of small components and faults. First, we create four medium-sized datasets for training component detection and classification models. Furthermore, we apply a series of effective data augmentation techniques to balance out the imbalanced classes. Finally, we propose the multi-stage component detection and classification based on the Single Shot Multibox detector and deep Residual Networks to detect small components and faults. The results show that the proposed system is fast and accurate in detecting common faults on power line components, including missing top caps, cracks in poles and cross arms, woodpecker damage on poles, and rot damage on cross arms. The field tests suggest that our system has a promising role in the intelligent monitoring and inspection of power line components and as a valuable addition to smart grids.

Automatic Segmentation and Enhancement of Pavement Cracks Based on 3D Pavement Images

Pavement cracking is a significant symptom of pavement deterioration and deficiency. Conventional manual inspections of road condition are gradually replaced by novel automated inspection systems. As a result, a great amount of pavement surface information is digitized by these systems with a high resolution. With pavement surface data, pavement cracks can be detected using crack detection algorithms. In this paper, a fully automated algorithm for segmenting and enhancing pavement crack is proposed, which consists of four major procedures. First, a preprocessing procedure is employed to remove spurious noise and rectify the original 3D pavement data. Second, crack saliency maps are segmented from 3D pavement data using steerable matched filter bank. Third, 2D tensor voting is applied to crack saliency maps to achieve better curve continuity of crack structure and higher accuracy. Finally, postprocessing procedures are used to remove redundant noises. The proposed procedures were evaluated over 200 asphalt pavement images with diverse cracks. The experimental results demonstrated that the proposed method showed a high performance and could achieve average precision of 88.38%, recall of 93.15%, and F-measure of 90.68%, respectively. Accordingly, the proposed approach can be helpful in automated pavement condition assessment.

An Automatic Surface Defect Inspection System for Automobiles Using Machine Vision Methods.

Automobile surface defects like scratches or dents occur during the process of manufacturing and cross-border transportation. This will affect consumers’ first impression and the service life of the car itself. In most worldwide automobile industries, the inspection process is mainly performed by human vision, which is unstable and insufficient. The combination of artificial intelligence and the automobile industry shows promise nowadays. However, it is a challenge to inspect such defects in a computer system because of imbalanced illumination, specular highlight reflection, various reflection modes and limited defect features. This paper presents the design and implementation of a novel automatic inspection system (AIS) for automobile surface defects which are the located in or close to style lines, edges and handles. The system consists of image acquisition and image processing devices, operating in a closed environment and noncontact way with four LED light sources. Specifically, we use five plane-array Charge Coupled Device (CCD) cameras to collect images of the five sides of the automobile synchronously. Then the AIS extracts candidate defect regions from the vehicle body image by a multi-scale Hessian matrix fusion method. Finally, candidate defect regions are classified into pseudo-defects, dents and scratches by feature extraction (shape, size, statistics and divergence features) and a support vector machine algorithm. Experimental results demonstrate that automatic inspection system can effectively reduce false detection of pseudo-defects produced by image noise and achieve accuracies of 95.6% in dent defects and 97.1% in scratch defects, which is suitable for customs inspection of imported vehicles.

Implementation of an Automatic Optical Inspection System for Solder Quality Classification of THT Solder Joints

Machine vision has been widely deployed in many industrial applications. However, the accuracy and maturity level for the inspection of through-hole technology (THT) solder joints have yet to reach its ultimate goal. In this paper, we have presented a detailed explanation on a set of novel algorithms that can be effectively used to implement an automatic vision system that is capable of classifying the quality of THT solder joints very precisely. This vision system can be easily integrated into a soldering robotic system that performs automatic soldering on THT solder joints. The proposed vision system is capable of controlling the entire robotic system while providing automatic rework capability on the defective solder joints with much less user interaction. The experimental results indicate that the proposed system has an improved recognition rate and good resilience to noise and uneven illumination distribution.

Retraction Notice: An automatic online inspection system for a coupler yoke for freight trains

Retraction Notice: An automatic online inspection system for a coupler yoke for freight trains

Pipeline Defect Detection Cloud System Using Role Encryption and Hybrid Information

Pipeline defect detection systems collect the videos from cameras of pipeline robots, however the systems always analyzed these videos by offline systems or humans to detect the defects of potential security threats. The existing systems tend to reach the limit in terms of data access anywhere, access security and video processing on cloud. There is in need of studying on a pipeline defect detection cloud system for automatic pipeline inspection. In this paper, we deploy the framework of a cloud based pipeline defect detection system, including the user management module, pipeline robot control module, system service module, and defect detection module. In the system, we use a role encryption scheme for video collection, data uploading, and access security, and propose a hybrid information method for defect detection. The experimental results show that our approach is a scalable and efficient defection detection cloud system.

A study of introduction of automatic product inspection systems using deep learning into production sites

A study of introduction of automatic product inspection systems using deep learning into production sites

Study on Development of Solid Oxide Fuel Cells' Automatic Nondestructive Inspection System Based on Terahertz Spectroscopy

Study on Development of Solid Oxide Fuel Cells' Automatic Nondestructive Inspection System Based on Terahertz Spectroscopy