Capacitor Detection in PCB Using YOLO Algorithm

Lin, Yih-Lon; Chiang, Yu-Min; Hsu, Hsiang-Chen

doi:10.1109/icsse.2018.8520170

Cited by 30 publications

(14 citation statements)

References 21 publications

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“…The anchor allocation rules proposed above are applied for the PCB assembly scene training dataset for which the anchor size has been determined. For the 52 × 52 output layer, there are 4 anchors allocated, which are [ 9 , 14 , 17 , 21 , 22 , 22 , 32 , 35 ]. For the 26 × 26 output layer, there are three allocated anchors, namely [ 27 , 30 , 34 , 41 , 47 , 47 ].…”

Section: Methodsmentioning

confidence: 99%

“…Therefore, for the joint module improved YOLOv3, we use the same anchor allocation method based on ERF. There are 5 anchors allocated to the 52 × 52 output layer, which are, respectively, [ 9 , 14 , 17 , 21 , 22 , 22 , 32 , 35 ], and [27, 41]. The 26 × 26 output layer assigns a total of 3 anchors, which are, respectively, [30, 47], [34, 47], and [186, 120].…”

Section: Methodsmentioning

confidence: 99%

“…This detection can assist in the realization of intelligent manufacturability decisions [ 20 ]. Lin et al proposed a YOLO-based capacitance detection method for PCB assembly, however, the detection object only contains nine types of capacitance [ 21 ]. Tao Xian et al proposed a CNN detection model for five defects likely to occur in the spring wire sockets during manufacturing.…”

Section: Related Workmentioning

confidence: 99%

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Research on Object Detection of PCB Assembly Scene Based on Effective Receptive Field Anchor Allocation

Chen

et al. 2022

Computational Intelligence and Neuroscience

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Vision-based object detection of PCB (printed circuit board) assembly scenes is essential in accelerating the intelligent production of electronic products. In particular, it is necessary to improve the detection accuracy as much as possible to ensure the quality of assembly products. However, the lack of object detection datasets in PCB assembly scenes is the key to restricting intellectual PCB assembly research development. As an excellent representative of the one-stage object detection model, YOLOv3 (you only look once version 3) mainly relies on placing predefined anchors on the three feature pyramid layers and realizes recognition and positioning using regression. However, the number of anchors distributed in each grid cell of different scale feature layers is usually the same. The ERF (effective receptive field) corresponding to the grid cell at different locations varies. The contradiction between the uniform distribution of fixed-size anchors and the ERF size range in different feature layers will reduce the effectiveness of object detection. Few people use ERF as a standard for assigning anchors to improve detection accuracy. To address this issue, firstly, we constructed a PCB assembly scene object detection dataset, which includes 21 classes of detection objects in three scenes before assembly, during assembly, and after assembly. Secondly, we performed a refined ERF analysis on each grid of the three output layers of YOLOv3, determined the ERF range of each layer, and proposed an anchor allocation rule based on the ERF. Finally, for the small and difficult-to-detect TH (through-holes), we increased the context information and designed improved-ASPP (Atrous spatial pyramid pooling) and channel attention joint module. Through a series of experiments on the object detection dataset of the PCB assembly scene, we found that under the framework of YOLOv3, anchor allocation based on ERF can increase mAP (mean average precision) from 79.32% to 89.86%. At the same time, our proposed method is superior to Faster R-CNN (region convolution neural network), SSD (single shot multibox detector), and YOLOv4 (you only look once version 4) in the balance of high detection accuracy and low computational complexity.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

Research on Object Detection of PCB Assembly Scene Based on Effective Receptive Field Anchor Allocation

Chen

et al. 2022

Computational Intelligence and Neuroscience

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“…Another neural network based study incorporates fuzzy rule-based method to correct any possible misclassification made by the neural network module [33]. In addition, recently there was a research which attempts to detect capacitor in PCB using YOLO algorithm [34]. However, prior studies are confined to either merely evaluating whether or not a component is flawed after detecting the mounted components or identifying each defect using a single-label classification approach.…”

Section: Smt Inspectionmentioning

confidence: 99%

MarsNet: Multi-Label Classification Network for Images of Various Sizes

et al. 2020

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Since the Convolutional Neural Network (CNN) has surfaced and fascinated the world, many researchers have exploited CNN for image classification, object detection, semantic segmentation, etc. However, the conventional CNNs have a pyramidal structure and were designed to process images which have the same size. Although some CNNs can accept images of various sizes, performance is degraded for images smaller than the size of images used for training. In this paper, we propose MarsNet, a CNN based end-to-end network for multi-label classification with an ability to accept various size inputs. In order to allow the network to accept such images, dilated residual network (DRN) is modified to get higher resolution feature maps, and horizontal vertical pooling (HVP) is newly designed to efficiently aggregate positional information from the feature maps. Furthermore, multi-label scoring module and threshold estimation module are employed to serve the purpose of multi-label classification. We verify the effectiveness of the proposed network through two distinctive experiments. We first verify our model by inspecting and classifying multiple types of defects occurred in PCB screen printer using solder paste inspection (SPI) datasets. Secondly, we verify our network using VOC 2007 dataset. Our network is pioneering in that no research has attempted to accomplish multi-label classification for defects in addition to being able to take input images of various sizes in SPI field. INDEX TERMS Convolutional neural networks, images of various sizes, multi-label classification, printed circuit board, solder paste inspection.

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“…The algorithm uses binary conversion to count 0's and 1's to find the region where a fault is present. Yih-Lon Lin, et al [9] present a method for capacitor detection in a PCB using YOLO. The authors trained the YOLOv2 network with 9 different kinds of capacitors.…”

Section: B Literature Surveymentioning

confidence: 99%

PCB-Fire: Automated Classification and Fault Detection in PCB

Khare

Bahel

Phadke

2020

2020 Third International Conference on Multimedia Processing, Communication &Amp; Information Technology (MPCIT)

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Printed Circuit Boards ("PCB") are the foundation for the functioning of any electronic device, and therefore are an essential component for various industries such as automobile, communication, computation, etc. However, one of the challenges faced by the PCB manufacturers in the process of manufacturing of the PCBs is the faulty placement of its components including missing components. In the present scenario the infrastructure required to ensure adequate quality of the PCB requires a lot of time and effort. The authors present a novel solution for detecting missing components and classifying them in a resourceful manner. The presented algorithm focuses on pixel theory and object detection, which has been used in combination to optimize the results from the given dataset.

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Capacitor Detection in PCB Using YOLO Algorithm

Cited by 30 publications

References 21 publications

Research on Object Detection of PCB Assembly Scene Based on Effective Receptive Field Anchor Allocation

Research on Object Detection of PCB Assembly Scene Based on Effective Receptive Field Anchor Allocation

MarsNet: Multi-Label Classification Network for Images of Various Sizes

PCB-Fire: Automated Classification and Fault Detection in PCB

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