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

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1 Advanced Materials Research Online: ISSN: , Vols , pp doi: / Trans Tech Publications, Switzerland A Template-Matching-Based Fast Algorithm for PCB Components Detection Haiming Yin Department of Computer Science and Technology, Jiaxing University, Jiaxing, Zhejiang, , China Haimingyin_hawk@hotmail.com Keywords: PCB detection, template matching, object alignment, coarse matching Abstract. 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. Introduction Printed circuit boards (PCB) provide the places for the electronic components as support material, and play a very important role in all electronic products. With the rapid development of electronic technology, the application fields are more and more wide. The research on how to detect the electronic components effectively to promote the electronics industry automation has gained much attention [1]. The exist algorithms about PCB electronic components detection can be divided into two classes: (1) reference image based; (2) non reference image. The former s procedure is as follows: given a standard electronic component as a target, the algorithm searches the specific area on the board for the target. These algorithms have been widely used for its simplicity to implement and high recognition accuracy. But the high complexity is a strong constraint which makes them only used in the off-line environment [2, 3, 4] and can t work well in the real-time environment [5]. In this paper, we propose a image-match based algorithm to solve the contradiction between detection accuracy and detection time which joint a coarse-match stage and a precise-match stage. The procedure of our algorithm is as follows: (1) model the target electronic component to a template image; (2) search in the certain area for the specific component to find a region with the highest similarity to the template image; (3) judge whether there is a component same as the target; (4) judge whether the component is welded rightly. The whole work procedure can be described as flow-chat Fig. 1. PCB electronic component detection PCB object alignment. Since our algorithm is based on image-match, the input image has to be preprocessed to align the detected region in the input image with the template image. After the detection system achieves the source image form the camera, system computes the background subtraction to get the foreground object, and then Hough transform is used to find the edge of the board, at last the image is rotated according the board-edge-line. To speed up the Hough transform procedure, we compute the rough edge points of the board by a simple scanning, and then use these detected edge points as the input of the Hough transform algorithm. This procedure can be depicted as Fig. 2. Coarse matching. In this match stage, a fast search algorithm is carried out to find the region with relatively high similarity for precise match. Assuming the size of a template image is M N, the center is that component in the input image is Pc(x,y), we search the location by moving a window (M N) around point Pc. To accelerate to search process, the input image is transformed to grey-scale image, and a 1/9 sampling method is also adopted. The similarity between the template image and the region image (in the window) is calculated by the Eq All rights reserved. No part of contents of this paper may be reproduced or transmitted in any form or by any means without the written permission of Trans Tech Publications, (ID: , Pennsylvania State University, University Park, USA-10/05/16,02:34:11)

2 3206 Materials Design, Processing and Applications Fig. 1 Flow-chat of the detection algorithm g = r * g *0.5 + b*0.11 (1) M N 2 1 = ( ( i, j) ( i, j) ), %3 = 0 & %3 = 0 S gs gd i j (2) i= 1 j= 1 Here S means the similarity, gs ( i, j) means the gray value of point (i,j) in the template image, gd ( i, j) means the gray value of point with the relative position (i,j) in input image. If the value of S1 of one region is large enough, the precise match will be conducted on this region. A. input image B. foreground image C. rough edge points Fig. 2 PCB alignment Precise matching. Precise match is done on RGB color space, and all the points in the windows are matched. Considering the fact that the color on the PCB are more likely take on dark-green of yellow-green, our algorithm restrain the green component when computing the similarity. The precise similarity of two images is calculated as Eq. 3.

3 Advanced Materials Research Vols M N ( i, j) ( i, j) ( i, j) ( i, j) ( i, j) ( i, j) S = ( r1 r2 ) *0.6 + ( g1 g2 ) *0.3 + ( b1 b2 ) *0.1 i= 1 j= 1 (3) When S2 is lesser than a certain threshold, the match is successful; it means there is the specific electronic component in this region, otherwise match fails and it means the specific electronic component is missed or welded wrongly. Experimental results and analysis In our experiments, three kinds of electronic components including resistor, inductor and capacitor are chosen, and each kind component is composed of 4 types. The test library consists of 100 pieces of boards and 600 electronic components. We take the commonly used items including recall, false alarm and precision to evaluate the efficiency of our algorithm. recall target ( d ) = t arg et ( t) (4) false _ alarm Error = total _ num (5) target ( d ) Precision = total _ num (6) target ( d ) target ( t ) Here, means the detected components num and means the total specific components num which need to be detected, false_alarm means the wrongly detected components num, total_num means the total times of detection. Table. 1 Experimental results electronic component type resistor inductor capacitor recall 96.2% 95.5% 97.1% false alarm 2.3% 3.2% 1.3% precision 95.3% 94.1% 96.5% The data in table 1 indicates that our algorithm can get good performance when detecting commonly used PCB components. The coarse match jointing precise match method greatly accelerate the detection speed; the mean time of detecting one component is lesser than second that can meet the requirement of industry product line. But in the experiments, we also find that this algorithm achieves low precision when dealing with small eletronic components. Our feature work is to enhance the algorithm and make it suitable for the detection of small eletronic components. Summary In this paper, we present a novel multi-level image matching-based method for the PCB electronic components detection. The coarse-precise detection model can greatly speed up the processing procedure. The experimental results show that this method can give good performance when used to detect common PCB electronic components. Acknowledgement This work was supported by Jiaxing Science and Technology Program, China ( 2012AY1012, 2012AY1026) and by Nonprofit Technology and Application Research Program of Zhejiang Province, China (2012C21020).

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