scholarly journals YOLOv4-MN3 for PCB Surface Defect Detection

2021 ◽  
Vol 11 (24) ◽  
pp. 11701
Author(s):  
Xinting Liao ◽  
Shengping Lv ◽  
Denghui Li ◽  
Yong Luo ◽  
Zichun Zhu ◽  
...  
Keyword(s):  
Defect Detection ◽  
High Performance ◽  
Surface Defect ◽  
Printed Circuit Board ◽  
Surface Defects ◽  
Activation Function ◽  
Production Quality ◽  
Circuit Board ◽  
Detection Accuracy ◽  
Surface Defect Detection

Surface defect detection for printed circuit board (PCB) is indispensable for managing PCB production quality. However, automatic detection of PCB surface defects is still a challenging task because, even within the same category of surface defect, defects present great differences in morphology and pattern. Although many computer vision-based detectors have been established to handle these problems, current detectors struggle to achieve high detection accuracy, fast detection speed and low memory consumption simultaneously. To address those issues, we propose a cost-effective deep learning (DL)-based detector based on the cutting-edge YOLOv4 to detect PCB surface defect quickly and efficiently. The YOLOv4 is improved upon with respect to its backbone network and the activation function in its neck/prediction network. The improved YOLOv4 is evaluated with a customized dataset, collected from a PCB factory. The experimental results show that the improved detector achieved a high performance, scoring 98.64% on mean average precision (mAP) at 56.98 frames per second (FPS), outperforming the other compared SOTA detectors. Furthermore, the improved YOLOv4 reduced the parameter space of YOLOv4 from 63.96 M to 39.59 M and the number of multiply-accumulate operations (Madds) from 59.75 G to 26.15 G.

The PCB Surface Defect Detection System Based on GPU Acceleration

2015 ◽  
Vol 713-715 ◽  
pp. 347-352
Author(s):  
Yu Liu ◽  
Si Zhe Li
Keyword(s):  
Defect Detection ◽  
Rapid Detection ◽  
Surface Defect ◽  
Printed Circuit Board ◽  
Detection System ◽  
Processing System ◽  
Circuit Board ◽  
Computing Platform ◽  
Surface Defect Detection ◽  
Time Requirements

Defective products are unavoidable in printed circuit board production process, so rapid detection and identificationmethods are badly in need of. PCB surface defect detection including a series of processing such as surface imagecapture, mixed noise filtering,images registering and so on, so it takes a lot of CPU time. To improve detection speed, based on GPU parallel computing platform, we designed a reasonable parallel processing system for PCB defect detectionto meet the need of real-time requirements of a production line. Experimental results show that parallel image processing algorithms based on GPU can achieve good results compared to the CPU-based serial algorithm (with speed up ratio up to8.34 in this paper),providing a new approachfor rapid detection of PCB surface defect.

scholarly journals Segmented Embedded Rapid Defect Detection Method for Bearing Surface Defects

Machines ◽  
2021 ◽  
Vol 9 (2) ◽  
pp. 40
Author(s):  
Linjian Lei ◽  
Shengli Sun ◽  
Yue Zhang ◽  
Huikai Liu ◽  
Hui Xie
Keyword(s):  
Machine Vision ◽  
Defect Detection ◽  
Character Recognition ◽  
Surface Defect ◽  
Surface Defects ◽  
Detection Method ◽  
Rapid Development ◽  
Detection Accuracy ◽  
Bearing Surface ◽  
Surface Defect Detection

The rapid development of machine vision has prompted the continuous emergence of new detection systems and algorithms in surface defect detection. However, most of the existing methods establish their systems with few comparisons and verifications, and the methods described still have various problems. Thus, an original defect detection method: Segmented Embedded Rapid Defect Detection Method for Surface Defects (SERDD) is proposed in this paper. This method realizes the two-way fusion of image processing and defect detection, which can efficiently and accurately detect surface defects such as depression, scratches, notches, oil, shallow characters, abnormal dimensions, etc. Besides, the character recognition method based on Spatial Pyramid Character Proportion Matching (SPCPM) is used to identify the engraved characters on the bearing dust cover. Moreover, the problem of characters being cut in coordinate transformation is solved through Image Self-Stitching-and-Cropping (ISSC). This paper adopts adequate real image data to verify and compare the methods and proves the effectiveness and advancement through detection accuracy, missing alarm rate, and false alarm rate. This method can provide machine vision technical support for bearing surface defect detection in its real sense.

Lightweight detection algorithm for fine-grained surface defects of aerospace seal rings

2021 ◽  
pp. 1-18
Author(s):  
Hui Liu ◽  
Boxia He ◽  
Yong He ◽  
Xiaotian Tao
Keyword(s):  
Feature Extraction ◽  
Defect Detection ◽  
Surface Defect ◽  
Surface Defects ◽  
Detection Algorithm ◽  
Seal Ring ◽  
Fine Grained ◽  
Aerospace Applications ◽  
Surface Defect Detection ◽  
The Mean

The existing seal ring surface defect detection methods for aerospace applications have the problems of low detection efficiency, strong specificity, large fine-grained classification errors, and unstable detection results. Considering these problems, a fine-grained seal ring surface defect detection algorithm for aerospace applications is proposed. Based on analysis of the stacking process of standard convolution, heat maps of original pixels in the receptive field participating in the convolution operation are quantified and generated. According to the generated heat map, the feature extraction optimization method of convolution combinations with different dilation rates is proposed, and an efficient convolution feature extraction network containing three kinds of dilated convolutions is designed. Combined with the O-ring surface defect features, a multiscale defect detection network is designed. Before the head of multiscale classification and position regression, feature fusion tree modules are added to ensure the reuse and compression of the responsive features of different receptive fields on the same scale feature maps. Experimental results show that on the O-rings-3000 testing dataset, the mean condition accuracy of the proposed algorithm reaches 95.10% for 5 types of surface defects of aerospace O-rings. Compared with RefineDet, the mean condition accuracy of the proposed algorithm is only reduced by 1.79%, while the parameters and FLOPs are reduced by 35.29% and 64.90%, respectively. Moreover, the proposed algorithm has good adaptability to image blur and light changes caused by the cutting of imaging hardware, thus saving the cost.

scholarly journals Research on a Surface Defect Detection Algorithm Based on MobileNet-SSD

2018 ◽  
Vol 8 (9) ◽  
pp. 1678 ◽  
Author(s):  
Yiting Li ◽  
Haisong Huang ◽  
Qingsheng Xie ◽  
Liguo Yao ◽  
Qipeng Chen
Keyword(s):  
Defect Detection ◽  
Surface Defect ◽  
Surface Defects ◽  
Detection Method ◽  
Detection Algorithm ◽  
Single Shot ◽  
Detection Model ◽  
Machine Learning Methods ◽  
Network Methods ◽  
Surface Defect Detection

This paper aims to achieve real-time and accurate detection of surface defects by using a deep learning method. For this purpose, the Single Shot MultiBox Detector (SSD) network was adopted as the meta structure and combined with the base convolution neural network (CNN) MobileNet into the MobileNet-SSD. Then, a detection method for surface defects was proposed based on the MobileNet-SSD. Specifically, the structure of the SSD was optimized without sacrificing its accuracy, and the network structure and parameters were adjusted to streamline the detection model. The proposed method was applied to the detection of typical defects like breaches, dents, burrs and abrasions on the sealing surface of a container in the filling line. The results show that our method can automatically detect surface defects more accurately and rapidly than lightweight network methods and traditional machine learning methods. The research results shed new light on defect detection in actual industrial scenarios.

Research on Surface Defect Detection Technique of Rolling Element Based on Computer Vision

2014 ◽  
Vol 1006-1007 ◽  
pp. 773-778 ◽  
Author(s):  
Chuan Ren ◽  
Xiao Yu Xiu ◽  
Guo Hui Zhou
Keyword(s):  
Computer Vision ◽  
Defect Detection ◽  
High Speed ◽  
Surface Defect ◽  
Surface Defects ◽  
Median Filter ◽  
Image Sensor ◽  
Threshold Method ◽  
Rolling Element ◽  
Surface Defect Detection

This paper proposed a new method of surface defect detection of rolling element based on computer vision, which adopted CCD digital camera as image sensor, and used digital image processing techniques to defect the surface defects of rolling element. The main steps include collect image, use an improved median filter to reduce the noise, increase or decrease the exposure to achieve the image enhancement, create a binary image with threshold method and detect the edge of the image, and use subtraction method for surface defects identification. The experiment indicates that the above methods the advantages of simple, the capability of noise resistance, high speed processing and better real-time.

scholarly journals A New Steel Defect Detection Algorithm Based on Deep Learning

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Weidong Zhao ◽  
Feng Chen ◽  
Hancheng Huang ◽  
Dan Li ◽  
Wei Cheng
Keyword(s):  
Deep Learning ◽  
Target Detection ◽  
Defect Detection ◽  
Steel Surface ◽  
Surface Defect ◽  
Detection Algorithm ◽  
Detection Accuracy ◽  
Average Precision ◽  
Original Algorithm ◽  
Surface Defect Detection

In recent years, more and more scholars devoted themselves to the research of the target detection algorithm due to the continuous development of deep learning. Among them, the detection and recognition of small and complex targets are still a problem to be solved. The authors of this article have understood the shortcomings of the deep learning detection algorithm in detecting small and complex defect targets and would like to share a new improved target detection algorithm in steel surface defect detection. The steel surface defects will affect the quality of steel seriously. We find that most of the current detection algorithms for NEU-DET dataset detection accuracy are low, so we choose to verify a steel surface defect detection algorithm based on machine vision on this dataset for the problem of defect detection in steel production. A series of improvement measures are carried out in the traditional Faster R-CNN algorithm, such as reconstructing the network structure of Faster R-CNN. Based on the small features of the target, we train the network with multiscale fusion. For the complex features of the target, we replace part of the conventional convolution network with a deformable convolution network. The experimental results show that the deep learning network model trained by the proposed method has good detection performance, and the mean average precision is 0.752, which is 0.128 higher than the original algorithm. Among them, the average precision of crazing, inclusion, patches, pitted surface, rolled in scale and scratches is 0.501, 0.791, 0.792, 0.874, 0.649, and 0.905, respectively. The detection method is able to identify small target defects on the steel surface effectively, which can provide a reference for the automatic detection of steel defects.

A Review Paper on Product Surface Defect Detection of Ironing Process

2016 ◽  
Vol 836 ◽  
pp. 147-152
Author(s):  
Akhmad Faizin ◽  
Arif Wahjudi ◽  
I. Made Londen Batan ◽  
Agus Sigit Pramono
Keyword(s):  
Image Processing ◽  
Surface Roughness ◽  
Defect Detection ◽  
Surface Defect ◽  
Surface Defects ◽  
Forming Process ◽  
Metal Forming Process ◽  
Surface Defect Detection ◽  
Product Surface ◽  
Processing Techniques

The quality of product of manufacturing industries depends on dimension accurately and surface roughness quality. There are many types of surface defects and levels of surface roughness quality. Ironing process is one type of metal forming process, which aims to reduce the wall thickness of the cup-shaped or pipes products, thus increasing the height of the wall. Manually surface inspection procedures are very inadequate to ensure the surface in guaranteed quality. To ensure strict requirements of customers, the surface defect inspection based on image processing techniques has been found to be very effective and popular over the last two decades. The paper has been reviewed some papers based on image processing for defect detection. It has been tried to find some alternatives of useful methods for product surface defect detection of ironing process.

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