Calculation of flexible printed circuit boards (FPC) global and local defect detection based on computer vision

Wang, Liya; Zhao, Yang; Zhou, Yaoming; Hao, Jingbin

doi:10.1108/cw-07-2014-0027

Cited by 38 publications

(17 citation statements)

References 2 publications

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“…Among them, SE is the lens conversion rate and c is the lens index value [31,32]. When a wonderful event occurs, in order to clearly describe the process of the wonderful event, the change of the frame image is accelerated, which leads to the strengthening of the lens movement.…”

Section: Image Recognitionmentioning

confidence: 99%

Research on Recognition Method of Basketball Goals Based on Image Analysis of Computer Vision

Zhang¹

2021

Journal of Sensors

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Moving target detection is involved in many engineering applications, but basketball has some difficulties because of the time-varying speed and uncertain path. The purpose of this paper is to use computer vision image analysis to identify the path and speed of a basketball goal, so as to meet the needs of recognition and achieve trajectory prediction. This research mainly discusses the basketball goal recognition method based on computer vision. In the research process, Kalman filter is used to improve the KCF tracking algorithm to track the basketball path. The algorithm of this research is based on MATLAB, so it can avoid the mixed programming of MATLAB and other languages and reduce the difficulty of interface design software. In the aspect of data acquisition, the extended EPROM is used to store user programs, and parallel interface chips (such as 8255A) can be configured in the system to output switch control signals and display and print operations. The automatic basketball bowling counter based on 8031 microprocessor is used as the host computer. After the level conversion by MAX232, it is connected with the RS232C serial port of PC, and the collected data is sent to the workstation recording the results. In order to consider the convenience of user operation, the GUI design of MATLAB is used to facilitate the exchange of information between users and computers so that users can see the competition results intuitively. The processing frame rate of the tested video image can reach 60 frames/second, more than 25 frames/second, which meet the real-time requirements of the system. The results show that the basketball goal recognition method used in this study has strong anti-interference ability and stable performance.

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Section: Image Recognitionmentioning

confidence: 99%

Research on Recognition Method of Basketball Goals Based on Image Analysis of Computer Vision

Zhang¹

2021

Journal of Sensors

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“…Structural methods include model matching [11] and morphological processing [12]. Threshold methods include iterative optimization method threshold [13], OTSU method [14], contrast adjustment threshold [15], watershed method [16] and so on. Spectrum methods usually include Fourier transform [17] and wavelet transform [18].…”

Section: Introductionmentioning

confidence: 99%

Defect Detection and Recognition of Mobile Phone Membrane Based on Convolutional Neural Network

Li¹,

Zhang²,

Liu³

2021

Proceedings of the 8th EAI International Conference on Green Energy and Networking, GreeNets 2021, June 6-7, 2021, Dalian, Peop

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With the upgrading of mobile phone equipment, automatic detection of mobile phone film defects has been paid more and more attention in industrial production quality. Mobile phone film defect detection is a huge workload and challenging problem. Traditional methods can also detect some industrial identification defects, but these methods can only detect defects under specific conditions, such as obvious defect outline, strong contrast, low noise conditions. The defect detection method of mobile phone film proposed in this paper is to locate the target area with input images obtained from the industrial environment, remove the background, and then classify them into their designated classes through convolutional neural network. Experimental results show that this method can meet the robustness and accuracy of mobile phone film defect detection.

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“…The structural method includes edge [12], skeleton [13], template match [14], and morphological operations [15]. The threshold methods include the iterative optimal threshold [16], Otsu method [17], contrast adjustment threshold method [18], Kittler method [19], and watershed method [20], etc. The spectral methods commonly include Fourier transform [21], wavelet transform [22], and Gabor transform [23].…”

Section: Introductionmentioning

confidence: 99%

Automatic Metallic Surface Defect Detection and Recognition with Convolutional Neural Networks

et al. 2018

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Automatic metallic surface defect inspection has received increased attention in relation to the quality control of industrial products. Metallic defect detection is usually performed against complex industrial scenarios, presenting an interesting but challenging problem. Traditional methods are based on image processing or shallow machine learning techniques, but these can only detect defects under specific detection conditions, such as obvious defect contours with strong contrast and low noise, at certain scales, or under specific illumination conditions. This paper discusses the automatic detection of metallic defects with a twofold procedure that accurately localizes and classifies defects appearing in input images captured from real industrial environments. A novel cascaded autoencoder (CASAE) architecture is designed for segmenting and localizing defects. The cascading network transforms the input defect image into a pixel-wise prediction mask based on semantic segmentation. The defect regions of segmented results are classified into their specific classes via a compact convolutional neural network (CNN). Metallic defects under various conditions can be successfully detected using an industrial dataset. The experimental results demonstrate that this method meets the robustness and accuracy requirements for metallic defect detection. Meanwhile, it can also be extended to other detection applications.

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Calculation of flexible printed circuit boards (FPC) global and local defect detection based on computer vision

Cited by 38 publications

References 2 publications

Research on Recognition Method of Basketball Goals Based on Image Analysis of Computer Vision

Research on Recognition Method of Basketball Goals Based on Image Analysis of Computer Vision

Defect Detection and Recognition of Mobile Phone Membrane Based on Convolutional Neural Network

Automatic Metallic Surface Defect Detection and Recognition with Convolutional Neural Networks

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