PCB Image Research Articles

PCB Image Defects Detection by Artificial Neural Networks and Resistance Analysis

The approach contains the sequence of algorithms and formulas for image processing. They are single-layer neural networks, thinning, clustering, mathematical image comparison, and measurements of the trace length and width. All these procedures solve the task of selection and separation of the main objects in the printed circuit board: contacts, traces, and defects. The calculated features connect the conductance resistance of traces with the characteristics of defects. Imposing a tolerance on the distributed or concentrated changes of resistance it is possible to mark the defective and suspicious printed circuit boards.

Defects Detection in PCB Images by Scanning Procedure, Flood-filling and Mathematical Comparison

The basis of the approach is a scanning procedure with the movement of windows on the printed circuit board to detect defects of various types. Mathematical image comparison, pixel distribution histograms, padding algorithms, statistical calculations, and histogram deviation measurements are applied to the small parts of the PCB image in a small window area. The paper considers K-mean clustering of pixel intensities to simplify the printed circuit board image, separation of elements on the printed circuit board image by filling with colors, determination of defect intensity, and subtraction formulas.

Defects Detection in PCB Images by Clustering, Rotation and Distributed Cumulative Histogram

The K-means clustering of pixels intensity to segment the PCB image to a binary form, hierarchical clustering and separation of elements on the PCB image, flood-filling of chains for their comparison with the reference ones, three methods of determination of the turn angle for alignment of the board, subtraction formulas, algorithms to rotate the image to its normal position, algorithms to build the image of the distributed cumulative histogram are considered in this paper.

Defects Detection in Printed Circuit Boards by Separation and Comparison of Chains

The known K-means clustering, flood-filling, and thinning algorithms are used to find coordinates of contacts in PCB images. Images of different types and colors are considered. The clustering algorithm is used to reduce the number of colors, to get uniform colors in the PCB image. The thinning algorithm is used to build skeletons and find pixels of contacts. The flood-fill algorithm is used to mark and separate chains, defect connectivity, and metal defects. Different subtraction operations are applied to original, transformed, and distributed cumulative histogram images.

Chains Defects Detection in a Printed Circuit Board Image by the Plane Partition and Flood-filling of Traces

An approach to dividing the printed circuit board into parts to increase visibility of defects in a PCB image is considered. The approach is based on a piecewise linear approximation of a cumulative histogram. The last one is calculated for numbers of informative pixels placed in rows and columns of an image matrix. Informative pixels are those indicating contacts and C traces. The histogram demonstrates a distribution of informative pixels along the OX and OY axes. The beginning and ending points of linear lines are taken as coordinates of the divided parts of the PCB board. The flood-fill algorithm is used to color and separate the PCB chains components. The start pixels are taken from a set of informative pixels. To measure defect values, a mean intensity and gradient functions for the template and manufactured images are used. Distributed cumulative histograms are applied to PCB components to detect places and intensity of defects.

Fast segmentation algorithm of PCB image using 2D OTSU improved by adaptive genetic algorithm and integral image

Fast segmentation algorithm of PCB image using 2D OTSU improved by adaptive genetic algorithm and integral image

PCB FAULT DETECTION BY USING CONVOLUTIONAL NEURAL NETWORKS

Convolutional neural network (CNN), a deep learning algorithm optimized for image processing due to its flexibility and efficiency, is proposed to be used in PCB defect detection via transfer learning. The proposed solution proved to be able to create an 85% accuracy CNN model that can predict the possible defects in each PCB image via a mobile phone. The model was then compared with previous solutions to determine whether the proposed solution was effective or not.

Depth Estimation of Monocular PCB Image Based on Self-Supervised Convolution Network

To improve the accuracy of using deep neural networks to predict the depth information of a single image, we proposed an unsupervised convolutional neural network for single-image depth estimation. Firstly, the network is improved by introducing a dense residual module into the encoding and decoding structure. Secondly, the optimized hybrid attention module is introduced into the network. Finally, stereo image is used as the training data of the network to realize the end-to-end single-image depth estimation. The experimental results on KITTI and Cityscapes data sets show that compared with some classical algorithms, our proposed method can obtain better accuracy and lower error. In addition, we train our models on PCB data sets in industrial environments. Experiments in several scenarios verify the generalization ability of the proposed method and the excellent performance of the model.

Image Processing Techniques for Inspection of Fault in PCB

Initially, the uncovered PCBs (PCB while not elements connected to it) were inspected unpredictably victimization manual examination systems, this involves human operators. This system is dear since it's extremely erring because of human error. The intention of this project so is to provide a uniquely affordable and Comprehensive defect detection technique. We have a tendency to apply the machine vision construct to examine the vacant PCB and we have a tendency to 1st compare a customary PCB image with a PCB image to be inspected employing an easy distinction algorithmic program that may sight the defected region. Image distinction methodology is one among the only ways for the examination of PCB defects.

Machine vision based online detection of PCB defect

The traditional PCB defect on-line detection has the problems of long detection time and poor accuracy of detection results. Therefore, a key technology of PCB defect online detection based on machine vision is proposed. Firstly, image retrieval is carried out by using visual detection algorithm, image smoothing, image contrast enhancement, image sharpening and other preprocessing operations are carried out by using gray-scale transformation algorithm to simplify the operation process and improve the image quality; secondly, PC is analyzed B. the causes and types of defects. The original color image is processed and binarized by the hybrid recognition method of mathematical morphology and pattern recognition. Then the reference image obtained by mathematical morphology method is used as a template for system self inspection. Finally, the image aberration detection algorithm is introduced to segment the threshold value of PCB defect image, remove redundant points and mark PCB image defect recognition results, improve the visual detection algorithm and optimize the hardware design to achieve PCB image defect detection and recognition. The experimental results show that the method has high detection accuracy and short detection time, and can effectively control the stable operation of the online detection system, which provides a reference for related research in this field.

A multiobjective metaheuristic approach for morphological filters on many-core architectures

Mathematical Morphology (MM) is a set-theoretic technique for the analysis of geometrical structures. It provides a powerful tool for image processing, but is hampered by significant computational requirements. These requirements can be substantially reduced by decomposing complex operators into sequences of simpler operators, at the cost of degradation of the quality of the results. This decomposition also directly translates to streaming task graphs, a programming model that maps well to the kind of systolic architectures typically associated with many-core systems. There is however a trade-off between mappings that implement high-quality filters and mappings that offer high performance in many-core systems. The approach presented in this paper exploits a multi-objective evolutionary algorithm as a design-time tool to investigate trade-offs between the quality of the MM decomposition and computational performance. The evolutionary process performs an analysis of filter quality vs computational performance and generates a set of task graphs and mappings that represent different trade-offs between the two objectives. It then outputs a Pareto front of mapping solutions, allowing the designer to select an implementation that matches application-specific requirements. The performance of the tool is benchmarked on a morphological filter for the detection of features in a high-resolution PCB image.

Automated PCB identification and defect-detection system (APIDS)

Ever growing PCB industry requires automation during manufacturing process to produce defect free products. Machine Vision is widely used as popular means of inspection to find defects in PCBs. However, it is still largely dependent on user input to select algorithm set for the PCB under inspection prior to the beginning of the process. Continuous increase in computation power of computers and image quality of image acquisition devices demands new methods for further automation. This paper proposes a new method to achieve further automation by identifying the type of PCB under inspection prior to begin defect inspection process. Identification of PCB is achieved by using local feature detectors SURF and ORB and using the orientation data acquired to transform the PCB image to the reference image for inspection of defects. A close-loop system is produced as a prototype to reflect the practicality of the idea. A Graphical User Interface was developed using MATLAB to present the proposed system. Test data contained 29 PCBs. Each PCB was tested 5 times for camera acquired images and 3 times for database images. The identification accuracy is 98.66% for database images and 100% for images acquired from the camera. The time taken to detect the model of PCB is recorded and is significantly lower for ORB based identification than SURF based. The system is also a close loop system which detects defects in PCB units. The detection of defects has highest accuracy of 92.3% for best controlled environment scenario. With controlled environment, the system could detect defects in PCB pertaining to smallest of components such as SMDs.

Detection of PCB Soldering Defects using Template Based Image Processing Method

In this study, a predefined template-based image processing system is proposed to automatically detect of PCB soldering defects that negatively affect circuit operation. The proposed system consists of a prototype, a camera, an image processing method and inspect process. The prototype is produced using a plastic material, depending on the focal length of the camera and the PCB size. Image processing step comprises two steps. Firstly, solder joints are determined using Fuzzy C-means clustering algorithm. Then, the center of each joint is determined. In the next step, a joint template is created that contains solder joints information. This joint template contains information about the effects of touching other joints for each joint. In this way, the inspection of soldering defects is getting shorter. Finally, each joint is only inspected for the joints specified in the template. The proposed method is evaluated on 85 real PCB image with 4250 soldering joints.

Detection method of PCB component based on automatic optical stitching algorithm

Purpose – This paper aims to propose image stitching by reduction of full line and taking line image as registration image to solve the problem of automatic optic inspection in PCB detection. In addition, surf registration was introduced for image stitching to improve the accuracy and speed of stitching. Design/methodology/approach – First, image stitching proceeded by method of full line reduction and taking line image as registration image; second, surf registration was introduced based on the traditional PCB image stitching algorithm. Scale space of the image pyramid was adopted for confirming relative future points between stitching image. The registration means of nearest neighbourhood and next neatest neighborhood was selected for feature matching and fused in region of interest to fulfil image stitching. Findings – The improved stitching algorithm with small data size of image, high speed and noncumulative transitive error eliminated displacement deviation and solved the stitching gap caused by uneven illumination, to greatly improve the accuracy and speed of stitching. Research limitations/implications – The research of this paper can only used for appearance detection and cannot be used for solder joint inspection with circuit detection or invisible solder joint detection; it can identify and mark PCB component defects but cannot classify automatically, thus artificial confirmation and processing is needed. Originality/value – Based on the traditional image stitching means, this paper proposed full line reduction for image stitching, which reduces processing of data and speeds up image stitching; in addition, surf registration was introduced into the study of PCB stitching algorithm, which greatly improves the accuracy and speed of stitching and solves stitching gap formed by opposite variation trend of image local edge caused by uneven illumination.

Mechanical Structure Design and Application for PCB Detection System

PCB defect detection based on AOI is the key technology to realize the automation production of PCB, and the mechanical structure design of the recognition system is one of the most important technologies in the detection system, too. By modeling the main hardware of mechanical structure of the system, we have integrated the whole hardware structure of the detection system. In this paper, we firstly design the general structure of the PCB recognition system. Then, we propose five axis positioning platform and PCB installation device. Finally, the designs of PCB belt conveyor and image acquisition device are completed. By using the integrated hardware structure combined with software control, we can realize intelligent recognition for PCB detection.

A Template-Matching-Based Fast Algorithm for PCB Components Detection

In this paper, a novel multi-level image matching-based method is proposed for the PCB components detection. After the registration of PCB image, a quick coarse matching is carried out to find the similar region with the standard component image in the certain region; and then a precise matching is conducted to judge whether it is the specific component. Our experimental results indicate this method can detect common PCB components with high speed and high precision.

Study on Image Position Algorithm of the PCB Detection

With the continuous development of electronic technology, automatic detection of the PCB board has become particularly important, and the key step of the detection is to identify and locate the location mark (usually it is a round). Hough transform is one of the most widely used circle detection method, but it is complex in computing and requires much resource. For these conditions, a new idea of circle detection is proposed to get the rough location of the circle with the template matching, and then the precise positioning is accomplished by an improved Hough transform which is to draw two non-parallel strings on the orientation round and the center of the circle shall locate the strings’ perpendicular intersection. Experiments show that the method is simple and effective, and can accomplish the PCB image positioning accurately.

A “L” PCB Image Detection Method Based on Feature

In this paper, an “L” detection is proposed to check PCB short circuit ,open circuit, raised detect and recessed detect in a Automatic Optic Inspection(AOI). This method were researched and simulated in MATLAB environment, the results showed that it can raise the efficiency and accuracy.

Embedded System for PCB Soldering Platform Controller Design

We make use of embedded BASIC Stamp 2 (BS2) microchip controller to design with Hopfield neural network (HNN) as the foundation of sample training, which applies for a soldering platform of mechanical vision and accomplishes PCB soldering positioning technology. The system is verified by Matlab and Simulink to set up the simulation of PCB image soldering positioning. The experiment is proved to improve the traditional low efficiency of PCB soldering technology, to achieve the feasibility of PCB image positioning and to promote the soldering quality.

Printed-Electronic PCB Circuit Inkjet Printing Control and Detection System

Printed electronic technology is focus in industry as an efficient, low consumption, energy saving, environmentally friendly technology. Ink-jet printing technology is the core of fully printed electronic technology. This article design a Printed electronic Assembly line system consisted of Fixed inkjet array, which can Implement high-speed inkjet printing. It Adopt the print driver architecture In accordance with WDM models which cross kernel and user level. Embedded with electronic circuit design software systems, the electronic circuit design is directly printed to PCB. Interlaced Fixed Multi-inkjet Has parallel high-speed printing features, reduce assembly line machinery vibration, simplify electromechanical servo systems, improve the accuracy of printing. The Design Consist of sections of PCB Positioning, Image servo, Matching printing and Quality detection.