MRD-Net: An Effective CNN-Based Segmentation Network for Surface Defect Detection

Lu, Pengwen; Ji, Jing; Huang, Yanqing

doi:10.1109/tim.2022.3200361

Cited by 17 publications

(8 citation statements)

References 44 publications

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“…Guo et al [25] introduced the spatial attention mechanism into U-Net, significantly improving the accuracy of the segmentation task. Lu et al [26] effectively addressed the challenge of diverse surface defects across various products by integrating multiscale features and an attention mechanism. Zhu et al [27] designed an attention mechanism that combines multi-frequency information and local cross-channel interaction to better represent and emphasize defect features.…”

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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“…algorithms through gradient calculation (that are responsive to adjacent pixel values changing rapidly), such as edge and shape detection [43]. Since these features are highly sensitive to noise and background information, they should include further abstract characterization, in order to allow accurate description and lower reconstruction error, but it is at the expense of more training data and inference time, due to the exponential growth of convolution and non-linear operations in deeper architectures [44].…”

Section: A Multi-scale and Differently Targetable Defectsmentioning

confidence: 99%

Deep Learning for Automatic Vision-Based Recognition of Industrial Surface Defects: A Survey

et al. 2023

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Automatic vision-based inspection systems have played a key role in product quality assessment for decades through the segmentation, detection, and classification of defects. Historically, machine learning frameworks, based on hand-crafted feature extraction, selection, and validation, counted on a combined approach of parameterized image processing algorithms and explicated human knowledge. The outstanding performance of deep learning (DL) for vision systems, in automatically discovering a feature representation suitable for the corresponding task, has exponentially increased the number of scientific articles and commercial products aiming at industrial quality assessment. In such a context, this article reviews more than 220 relevant articles from the related literature published until February 2023, covering the recent consolidation and advances in the field of fully-automatic DL-based surface defects inspection systems, deployed in various industrial applications. The analyzed papers have been classified according to a bi-dimensional taxonomy, that considers both the specific defect recognition task and the employed learning paradigm. The dependency on large and high-quality labeled datasets and the different neural architectures employed to achieve an overall perception of both well-visible and subtle defects, through the supervision of fine or/and coarse data annotations have been assessed. The results of our analysis highlight a growing research interest in defect representation power enrichment, especially by transferring pre-trained layers to an optimized network and by explaining the network decisions to suggest trustworthy retention or rejection of the products being evaluated.

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“…In this study, a comparative analysis is conducted between the proposed LMFD method and other state-ofthe-art techniques on the 2D-HeLa dataset. The techniques considered for comparison include popular methods such as LBP, LPQ (Local Phase Quantization) 48 , LTP (Local Ternary Patterns) 49 , MRD (Multiscale feature fusion and Reverse attention network for Detection) 50 , and disCLBP (Discriminative Completed Local Binary Patterns) 51 . The average accuracy in more than 5-fold cross-validation is also evaluated using a suitable metric 52 .…”

Section: Applicability For Texture Feature Classificationmentioning

confidence: 99%

LMFD: lightweight multi-feature descriptors for image stitching

Fan,

Mao,

et al. 2023

Sci Rep

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Image stitching is a fundamental pillar of computer vision, and its effectiveness hinges significantly on the quality of the feature descriptors. However, the existing feature descriptors face several challenges, including inadequate robustness to noise or rotational transformations and limited adaptability during hardware deployment. To address these limitations, this paper proposes a set of feature descriptors for image stitching named Lightweight Multi-Feature Descriptors (LMFD). Based on the extensive extraction of gradients, means, and global information surrounding the feature points, feature descriptors are generated through various combinations to enhance the image stitching process. This endows the algorithm with formidable rotational invariance and noise resistance, thereby improving its accuracy and reliability. Furthermore, the feature descriptors take the form of binary matrices consisting of 0s and 1s, not only facilitating more efficient hardware deployment but also enhancing computational efficiency. The utilization of binary matrices significantly reduces the computational complexity of the algorithm while preserving its efficacy. To validate the effectiveness of LMFD, rigorous experimentation was conducted on the Hpatches and 2D-HeLa datasets. The results demonstrate that LMFD outperforms state-of-the-art image matching algorithms in terms of accuracy. This empirical evidence solidifies the superiority of LMFD and substantiates its potential for practical applications in various domains.

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MRD-Net: An Effective CNN-Based Segmentation Network for Surface Defect Detection

Cited by 17 publications

References 44 publications

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

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

Deep Learning for Automatic Vision-Based Recognition of Industrial Surface Defects: A Survey

LMFD: lightweight multi-feature descriptors for image stitching

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