Design of LQFP chip pin defect detection system based on machine vision

Ou, XingFu; Chen, Wenfeng; Zhang, Miao

doi:10.1109/mlise54096.2021.00021

Cited by 2 publications

(2 citation statements)

References 3 publications

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“…Feature extraction is a key step in image processing and classification, extracting texture and geometric feature vectors of defects for analysis [2]. The surface defect image of the crankshaft is a data set composed of pixels [3]. In feature extraction, the common features of target objects are found in the image to provide the basis for image classification.…”

Section: Surface Defect Characterizationmentioning

confidence: 99%

Crankshaft defect detection based on machine vision

Guo

Sun

2023

J. Phys.: Conf. Ser.

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As a core component of the Marine engine, the crankshaft crank is required to have a high surface quality to ensure that the ship does not fail during the voyage. After analysing the defect characteristics, aiming at the problems such as the inaccurate detection of single defect feature in the crankshaft bend defect, which leads to poor detection effect, a crankshaft bend defect detection classification method based on machine vision was proposed. The recognition accuracy can reach 97.8%. Compared with the defect identification by texture and geometric features alone, the classification and recognition accuracy can be improved, and the requirements of industrial detection can be better met.

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Section: Surface Defect Characterizationmentioning

confidence: 99%

Crankshaft defect detection based on machine vision

Guo

Sun

2023

J. Phys.: Conf. Ser.

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“…Li et al [19] proposed a gray-scale feature-based template matching method for connector pin skew detection, with a detection accuracy better than 0.06 mm, but the method is cumbersome and computationally intensive, and requires the production of different connector pin templates. Ou et al [20] proposed a fast segmentation algorithm based on dynamic thresholding to obtain chip feature images, and designed a pin defect detection method based on gray-scale jump detection. This method uses a point set alignment algorithm to calculate the pin deviation, which is computationally complex and susceptible to interference from noise points.…”

Section: Connector Imagesmentioning

confidence: 99%

Research on the examination technology of connector pin skewing according to Blob analysis^*

Pan,

Li,

et al. 2023

Meas. Sci. Technol.

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Pin skew detection is an important means to ensure the reliable operation of connectors. To address the issues of low accuracy and limited applicability in existing research, this paper proposes a connector pin skew detection method based on Blob analysis. Firstly, the image is segmented by incorporating the dimensional features of the tested connector to retain the effective information region in the image, reducing the computational workload for subsequent image processing. The image is preprocessed using an improved median filtering algorithm to effectively mitigate the interference of noise on the detection process. Secondly, a locally adaptive approach is employed to dynamically adjust the threshold, and morphological processing is applied to the pin image to enhance the pin speckle features. Subsequently, Blob analysis is utilized to analyze the connector pin speckles, obtaining data on the pin skew. Different evaluation criteria for pin skew data of various connectors are established to achieve quantitative assessment. Finally, experiments are conducted for pin skew detection of single-hole rectangular, double-hole rectangular, and single-hole circular connectors. The experimental results demonstrate that the proposed connector pin skew detection method can effectively detect various types of pin skew in connectors, with a detection accuracy better than 0.05 mm and a repeatability better than 0.03 mm. This method is suitable for automatic detection scenarios of connector pin skew.

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Design of LQFP chip pin defect detection system based on machine vision

Cited by 2 publications

References 3 publications

Crankshaft defect detection based on machine vision

Crankshaft defect detection based on machine vision

Research on the examination technology of connector pin skewing according to Blob analysis^*

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Design of LQFP chip pin defect detection system based on machine vision

Cited by 2 publications

References 3 publications

Crankshaft defect detection based on machine vision

Crankshaft defect detection based on machine vision

Research on the examination technology of connector pin skewing according to Blob analysis*

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Research on the examination technology of connector pin skewing according to Blob analysis^*