Application of improved Otsu threshold segmentation algorithm in mobile phone screen defect detection

Liu, Meiju; Rui, Zhuang; Guo, Xifeng; Junrui, Zhao

doi:10.1109/ccdc49329.2020.9164875

Cited by 11 publications

(6 citation statements)

References 2 publications

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“…Yang et al [14] combined a supervised multi-threshold segmentation model with the Canny edge detector to effectively recognize similar features on the surface of the target object that are otherwise difficult to differentiate. Meiju et al [15] proposed a two-dimensional Otsu segmentation algorithm for small defects in mobile phone screens, which achieves accurate segmentation of target and background. While these methods have achieved significant results in specific scenarios, more refined detection strategies are still required to address the complex situation of mobile phone backplane defects with varying scales and similar backgrounds.…”

Section: Related Workmentioning

confidence: 99%

Research on Mobile Phone Backplane Defect Segmentation Based on MDAF-UNet

Chen,

Min

2024

Electronics

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Mobile phone backplanes are an important part of mobile phones, and are often affected by a wide range of factors during the manufacturing process, resulting in defects of various scales and similar backgrounds. Therefore, accurately identifying these defects is crucial for improving mobile phone quality. To address this challenge, this paper proposes a multi-scale and dynamic attention fusion UNet (MDAF-UNet) model. The model innovatively combines normal convolution with dilated convolution. This allows the model to capture subtle features of defects and to perceive a larger range of feature variations. Moreover, an improved attention mechanism is introduced in this paper. It fuses channel attention and spatial attention, and dynamically adjusts the feature fusion strategy with learnable weights. This allows the model to increase the attention of important features and improve the effectiveness of feature representation. Experimental results on a publicly available dataset show that the MDAF-UNet model achieves 66.9% Mean Intersection over Union (MIoU), outperforming other state-of-the-art models. This result provides an effective solution to the mobile phone backplane defect segmentation problem.

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Section: Related Workmentioning

confidence: 99%

Research on Mobile Phone Backplane Defect Segmentation Based on MDAF-UNet

Chen,

Min

2024

Electronics

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“…where σd is the distance weight coefficient and σr is the pixel similarity weight coefficient Finally, the neighborhood S of the coordinate point is weighted and filtered to replac the initial value of the point, as shown in Equation (7):…”

Section: Image Denoisingmentioning

confidence: 99%

“…Additionally, if the neural network model is complex, it is less likely to achieve online real-time defect detection [4]. Therefore, traditional machine vision-based defect detection algorithms (MV-DDA) are still widely applied to various products, such as steel [5], fabric [6], and electronic devices [7].…”

Section: Introductionmentioning

confidence: 99%

Defects Detection of Lithium-Ion Battery Electrode Coatings Based on Background Reconstruction and Improved Canny Algorithm

Wang,

Liu,

Zhang

et al. 2024

Coatings

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Aiming to address the problems of uneven brightness and small defects of low contrast on the surface of lithium-ion battery electrode (LIBE) coatings, this study proposes a defect detection method that combines background reconstruction with an enhanced Canny algorithm. Firstly, we acquire and pre-process the electrode coating image, considering the characteristics of the electrode coating process and defects. Secondly, background reconstruction and the difference method are introduced to achieve the rough localization of coating defects. Furthermore, the image with potential defects undergoes enhancement through improved Gamma correction, and the PSO-OTSU algorithm with adaptive searching is applied to determine the optimal segmentation. Finally, precise defect detection is accomplished using the improved Canny algorithm and morphological processing. The experimental results show that, compared with the maximum entropy method, the region growth method, and the traditional Canny algorithm, the algorithm in this paper has a higher segmentation accuracy for defects. It better retains defect edge features and provides a more accurate detection effect for defects like scratches, dark spots, bright spots, metal leakage, and decarburization, which are difficult to recognize on the background of coating areas of electrodes. The proposed method is suitable for the online real-time defect detection of LIBE coating defects in actual lithium-ion battery industrial production.

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“…With the development of IR imaging and computer vision [1], image segmentation which aims to extract object of interest from image plays an essential Minjie Wan E-mail: minjiewan1992@njust.edu.cn Guohua Gu E-mail: gghjust@mail.njust.edu.cn 1 School of Electronic and Optical Engineering, Nanjing University of Science and Technology, Nanjing 210094, China 2 Shanghai Institute of Spaceflight Control Technology, Shanghai 201109, China role in many areas of both civil and military applications, such as geology exploration [2], aerospace engineering [3], security monitoring [4] and so on. Among various image segmentation methods [5][6][7][8], ACM has gained popularity because of its excellent ability to obtain closed contours with sub-pixel accuracy [9]. Although, a number of ACMs [10][11][12][13] have achieved satisfactory performances in clear visible images, they only use single image feature information to construct the energy function.…”

Section: Introductionmentioning

confidence: 99%

“…Nowadays, IR image segmentation which extracts the object of interest from image plays a fundamental role for all-day object detection and tracking. Among various image segmentation methods [5,6,7,8], ACM has gained popularity because of its excellent ability to obtain closed contours with sub-pixel accuracy [9]. Although, a number of ACMs [10,11,12,13] have achieved satisfactory performances in clear visible images, they only use single image feature information to construct the energy function.…”

Section: Introductionmentioning

confidence: 99%

Multi-Feature Driven Active Contour Segmentation Model for Infrared Image With Intensity Inhomogeneity

Huang¹,

Zhou²,

Wan³

et al. 2021

Preprint

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Active contour model (ACM) is one of the most widely used image segmentation tools at present, but the existing methods only utilize single feature information of image to minimize the energy function, which is easy to cause false segmentations in infrared (IR) images. In this paper, we propose a multi-feature driven active contour segmentation model to handle IR images with intensity inhomogeneity. Firstly, an especially-designed signed pressure force (SPF) function is constructed by combining the global information calculated by global average gray information and the local multi-feature information calculated by local entropy, local standard deviation and gradient information. Then, we draw upon adaptive weight coefficient computed by local range to incorporate the afore-mentioned global term and local term. Next, the SPF function is substituted into the level set formulation (LSF) for further evolution. Finally, the LSF converges after a finite number of iterations and the IR image segmentation result is obtained from the corresponding convergence result. Experimental results demonstrate that the presented method outperforms typical models in terms of precision rate and overlapping rate in IR test images.

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Application of improved Otsu threshold segmentation algorithm in mobile phone screen defect detection

Cited by 11 publications

References 2 publications

Research on Mobile Phone Backplane Defect Segmentation Based on MDAF-UNet

Research on Mobile Phone Backplane Defect Segmentation Based on MDAF-UNet

Defects Detection of Lithium-Ion Battery Electrode Coatings Based on Background Reconstruction and Improved Canny Algorithm

Multi-Feature Driven Active Contour Segmentation Model for Infrared Image With Intensity Inhomogeneity

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