Online learning method based on support vector machine for metallographic image segmentation

Li, Mingchun; Chen, Dali; Liu, Shixin; Guo, Dinghao

doi:10.1007/s11760-020-01778-1

Cited by 11 publications

(4 citation statements)

References 22 publications

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“…where σ represents the minimum sample weight and is set to 0. With the pyramid level weight w l , Equations (5) and 7are augmented into Equations (9) and (10), respectively. (10) where p + is the set of positive anchor points.…”

Section: Network Structure and Loss Of Sasapdmentioning

confidence: 99%

“…The rule-based methods could offer accurate segmentation results, but often involve the prior rules, which greatly limit the generality in other applications [ 1 , 2 ]. The learning-based methods work based on handcrafted features, but they always suffer from the sensitively to constructed features for metallographic images with complex features [ 3 , 4 , 5 ]. Owing to the powerful ability of automatically learning the discriminable features, the recent surge of interest in deep learning methods has appeared in material science [ 6 , 7 , 8 , 9 ].…”

Section: Introductionmentioning

confidence: 99%

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Quantitative Analysis of Metallographic Image Using Attention-Aware Deep Neural Networks

Zhang

et al. 2020

Sensors

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As a detection tool to identify metal or alloy, metallographic quantitative analysis has received increasing attention for its ability to evaluate quality control and reveal mechanical properties. The detection procedure is mainly operated manually to locate and characterize the constitution in metallographic images. The automatic detection is still a challenge even with the emergence of several excellent models. Benefiting from the development of deep learning, with regard to two different metallurgical structural steel image datasets, we propose two attention-aware deep neural networks, Modified Attention U-Net (MAUNet) and Self-adaptive Attention-aware Soft Anchor-Point Detector (SASAPD), to identify structures and evaluate their performance. Specifically, in the case of analyzing single-phase metallographic image, MAUNet investigates the difference between low-frequency and high-frequency and prevents duplication of low-resolution information in skip connection used in an U-Net like structure, and incorporates spatial-channel attention module with the decoder to enhance interpretability of features. In the case of analyzing multi-phase metallographic image, SASAPD explores and ranks the importance of anchor points, forming soft-weighted samples in subsequent loss design, and self-adaptively evaluates the contributions of attention-aware pyramid features to assist in detecting elements in different sizes. Extensive experiments on the above two datasets demonstrate the superiority and effectiveness of our two deep neural networks compared to state-of-the-art models on different metrics.

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Section: Network Structure and Loss Of Sasapdmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Quantitative Analysis of Metallographic Image Using Attention-Aware Deep Neural Networks

Zhang

et al. 2020

Sensors

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“…Chen, Guo et al [4] developed an instance segmentation method for aluminum alloy microstructures and compared the effects of five different loss functions on segmentation performance. Li, Chen et al [5] proposed an online learning method based on support vector machines for microstructure image segmentation, effectively extracting target features. Chen, Li et al [6] achieved precise segmentation of aluminum alloy microstructures using the superpixel algorithm and the idea of transfer learning.…”

Section: Introductionmentioning

confidence: 99%

Weakly supervised aluminum alloy microstructure image semantic segmentation based on consistency constraint

Feng,

Zhao

2024

Third International Conference on Electronic Information Engineering, Big Data, and Computer Technology (EIBDCT 2024)

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To improve the quality of alloys, experts need to intelligently analyze the metallographic images, where semantic segmentation serves as a vital means of achieving this analysis. However, traditional fully supervised semantic segmentation methods often suffer from high annotation costs. Weakly supervised semantic segmentation methods require only simple image-level annotations to achieve high-precision semantic segmentation. This paper constructs a dataset of weakly supervised microstructure images of aluminum alloys, greatly reducing manpower and time costs. Furthermore, addressing the sparsity and incompleteness of Class Activation Maps (CAMs) in traditional weakly supervised methods, a weakly supervised semantic segmentation method for aluminum alloy microstructure images is proposed based on consistency constraints. The method first introduces consistency constraints in the feature extraction process to improve the quality of CAMs. It then uses the improved CAMs as a target cue to generate affinity labels and employs the AffinityNet network and random walk strategy to mine target pixels in non-discriminatory areas, obtaining completer and more detailed pixel-level pseudo labels. Finally, semantic segmentation of the obtained pixel-level pseudo labels is performed using the U-Net network to obtain the final segmentation result. Extensive experimental results demonstrate that our method effectively improves the quality of CAMs, thereby enhancing the performance of the segmentation network.

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“…ML encoder-decoder networks and CNNs have also been used in the past for binary or RGB segmentations for microstructural characterization. For example, the U-Net encoderdecoder network was used to obtain binary segmentations of aluminum alloys [24]. Also, Fully Convolutional Neural Networks (FCNNs), and Residual Neural Networks (RNNs) have been implemented to characterize the shape of microstructures as "lamellar", "duplex", or "acicular", to then obtain binary segmentations [25,26].…”

Section: Introductionmentioning

confidence: 99%

Optimized and autonomous machine learning framework for characterizing pores, particles, grains and grain boundaries in microstructural images

Perera,

Guzzetti,

Agrawal

2021

Preprint

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Additively manufactured metals exhibit heterogeneous microstructure which dictates their material and failure properties. Experimental microstructural characterization techniques generate a large amount of data that requires expensive computationally resources. In this work, an optimized machine learning (ML) framework is proposed to autonomously and efficiently characterize pores, particles, grains and grain boundaries (GBs) from a given microstructure image. First, using a classifier Convolutional Neural Network (CNN), defects such as pores, powder particles, or GBs were recognized from a given microstructure. Depending on the type of defect, two different processes were used. For powder particles or pores, binary segmentations were generated using an optimized Convolutional Encoder-Decoder Network (CEDN). The binary segmentations were used to used obtain particle and pore size and bounding boxes using an object detection ML network (YOLOv5). For GBs, another optimized CEDN was developed to generate RGB segmentation images, which were used to obtain grain size distribution using two regression CNNS. To optimize the RGB CEDN, the Deep Emulator Network SEarch (DENSE) method which employs the Covariance Matrix Adaptation -Evolution Strategy (CMA-ES) was implemented. The optimized RGB segmentation network showed a substantial reduction in training time and GPU usage compared to the unoptimized network, while maintaining high accuracy. Lastly, the proposed framework showed a significant improvement in analysis time when compared to conventional methods.

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Online learning method based on support vector machine for metallographic image segmentation

Cited by 11 publications

References 22 publications

Quantitative Analysis of Metallographic Image Using Attention-Aware Deep Neural Networks

Quantitative Analysis of Metallographic Image Using Attention-Aware Deep Neural Networks

Weakly supervised aluminum alloy microstructure image semantic segmentation based on consistency constraint

Optimized and autonomous machine learning framework for characterizing pores, particles, grains and grain boundaries in microstructural images

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