A novel approach of standard data base generation for defect detection in bare PCB

Kumar, M. S. Mohan; Singh, Niraj Kumar; Kumar, Manjesh; Vishwakarma, Ajay

doi:10.1109/ccaa.2015.7148363

Cited by 14 publications

(11 citation statements)

References 3 publications

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“…By constructing a circle to simplify the calculation process, the actual position of the industrial camera shooting chassis can be mapped to the shooting plane. Then the rotation angle can be expressed by Equation (5) (5) where j is j -class chassis; ( , ) Then, the chassis image is detected by Mask R-CNN. The process has been described in detail in the previous section.…”

Section: Chassis_namementioning

confidence: 99%

“…The template matching algorithm locates the position of a particular object in the image and then identifies the object. Kumar et al [5] proposed a detection algorithm for image enhancement and standard template generation to automatically detect reference matching defects; the detection time of the algorithm is as short as 14 ms. Kim et al used a feature matching defect detection method to determine the corresponding relationship between feature sets to detect faults [6]. Huang et al proposed a standard machine assembly quality machine vision method based on One Versus Rest One Versus Rest (OVR-SVM) and realized the assembly quality evaluation of standard components based on the support vector machine by using the One Versus Rest (OVR) strategy [7].…”

Section: Introductionmentioning

confidence: 99%

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Chassis Assembly Detection and Identification Based on Deep Learning Component Instance Segmentation

Liu

et al. 2019

Symmetry

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Chassis assembly quality is a necessary step to improve product quality and yield. In recent years, with the continuous expansion of deep learning method, its application in product quality detection is increasingly extensive. The current limitations and shortcomings of existing quality detection methods and the feasibility of improving the deep learning method in quality detection are presented and discussed in this paper. According to the characteristics of numerous parts and complex types of chassis assembly components, a method for chassis assembly detection and identification based on deep learning component segmentation is proposed. In the proposed method, assembly quality detection is first performed using the Mask regional convolutional neural network component instance segmentation method, which reduces the influence of complex illumination conditions and background detection. Next, a standard dictionary of chassis assembly is built, which is connected with Mask R-CNN in a cascading way. The component mask is obtained through the detection result, and the component category and assembly quality information is extracted to realize chassis assembly detection and identification. To evaluate the proposed method, an industrial assembly chassis was used to create datasets, and the method is effective in limited data sets of industrial assembly chassis. The experimental results indicate that the accuracy of the proposed method can reach 93.7%. Overall, the deep learning method realizes complete automation of chassis assembly detection.

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Section: Chassis_namementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Chassis Assembly Detection and Identification Based on Deep Learning Component Instance Segmentation

Liu

et al. 2019

Symmetry

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“…Several methodologies have been introduced to segment and extract the PCB solder joints using digital image processing technology. These methods include multithreshold segmentation method, 5 histogram-based feature analysis methods, 10,39 template matching methods, 2,36,38 gray-based projection methods, 16,19 and solder joint detection methods based on color space. 9,37 The histogram-based feature analysis method is achieved by extracting and analyzing the histogram of solder joints.…”

Section: Related Workmentioning

confidence: 99%

“…9,37 The histogram-based feature analysis method is achieved by extracting and analyzing the histogram of solder joints. 10,39 The template matching method is accurate but di±cult to operate. 2,36,38 The gray-based projection method uses the color threshold to bifurcate the solder joint image and extract the Xand Y -axis projection of the binary image.…”

Section: Related Workmentioning

confidence: 99%

“…Although these methods can be e®ective at locating solder joints, but only if welding them one by one, they cannot meet the requirements of locating the solder joints of the entire image at the same time and still have a high precision. 2,[5][6][7]9,10,[15][16][17][19][20][21]24,[33][34][35][36][37][38][39][40] Combined with the characteristics of the small PCB size, the large number of solder joints, the small single spot area and the compressed welded, the traditional methods mentioned classify and locate the solder joints one by one so that they are not suitable for the extraction of various solder joints. 3,14,26,28,30,32 Compared with existing works on PCB solder joint extraction and inspection, the novelty and advantages from this paper are as follows:…”

Section: Related Workmentioning

confidence: 99%

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A Novel Multi-Pattern Solder Joint Simultaneous Segmentation Algorithm for PCB Selective Packaging Systems

Huang

Shen

Xie

et al. 2019

Int. J. Patt. Recogn. Artif. Intell.

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To prevent any negative electromagnetic influence of high-density integrated circuits, an insulation package needs to be specially designed to shield it. Aiming at the low efficiency and material waste in traditional packaging methods, a printed circuit board (PCB) selective packaging system based on a multi-pattern solder joint simultaneous segmentation algorithm and three-dimensional printing technology is introduced in this paper. Firstly, the structure of PCB selective packaging system is designed. Secondly, to solve the existing problems, such as multi-pattern solder joints which are located densely in small welding areas and are hard to be extracted in the small-area integrated circuit board, a multi-pattern solder joint simultaneous segmentation algorithm is developed based on (geometrical) neighborhood features to extract and locate the optimal PCB solder joint areas. Finally, tests using three actual PCB are carried out to compare the proposed method with traditional multi-threshold solder joint extraction methods. Test results indicate that the proposed algorithm is simple and effective. Diverse solder joints can be optimally located and simultaneously extracted from the collected PCB image, which greatly improves the filling rate of the solder joint areas and filters out false pixels. Thus, this method provides a reliable location-finding tool to help place solder points in PCB selective packaging systems.

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Sparse Block DETR: Precise and Speedy End-to-End Detector for PCB Defect Detection

Hong,

Xie,

Yang

2023

Lecture Notes in Computer Science

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A novel approach of standard data base generation for defect detection in bare PCB

Cited by 14 publications

References 3 publications

Chassis Assembly Detection and Identification Based on Deep Learning Component Instance Segmentation

Chassis Assembly Detection and Identification Based on Deep Learning Component Instance Segmentation

A Novel Multi-Pattern Solder Joint Simultaneous Segmentation Algorithm for PCB Selective Packaging Systems

Sparse Block DETR: Precise and Speedy End-to-End Detector for PCB Defect Detection

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