An Image Generation Method of Unbalanced Ship Coating Defects Based on IGASEN-EMWGAN

Bu, Henan; Hu, Changzhou; Yuan, Xin; Ji, Xingyu; Lyu, Hongyu; Zhou, Honggen

doi:10.3390/coatings13030620

Cited by 3 publications

(1 citation statement)

References 62 publications

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“…Still, it also has anti-fouling, beautification, decoration, and other special functions [2]. The coating quality is directly related to the ship's construction cycle and maintenance costs, as well as an essential factor affecting the hull's corrosion resistance and the ship's service life [3]. However, in the process of ship coating construction, due to the joint influence of many factors such as process parameters, internal and external environment, and coating quality, many different types of coating defects such as sagging, blistering, cracking, delamination, etc.…”

Section: Introductionmentioning

confidence: 99%

A Zero-Shot Image Classification Method of Ship Coating Defects Based on IDATLWGAN

Bu,

Yang,

et al. 2024

Coatings

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In recent years, the defect image classification method based on deep transfer learning has been widely explored and researched, and the task of source and target domains with the same painting defect image class has been solved successfully. However, in real applications, due to the complexity and uncertainty of ship painting conditions, it is very likely that there are unknown classes of painting defects, and the traditional deep learning model cannot identify a few classes, which leads to model overfitting and reduces its generalization ability. In this paper, a zero-shot Image classification method for ship painting defects based on IDATLWGAN is proposed to identify new unknown classes of defects in the target domain. The method is based on a deep convolutional neural network combined with adversarial transfer learning. First, a preprocessed ship painting defect dataset is used as input for the domain-invariant feature extractor. Then, the domain invariant feature extractor takes domain invariant features from the source and target domains. Finally, Defect discriminators and domain alignment discriminators are employed to classify the known categories of unlabeled defects and unknown categories of unlabeled defects in the target domain and to further reduce the distance between the edge distributions of the source and target domains. The experimental results show that the proposed model in this paper extracts a better distribution of invariant features in the source and target domains compared to other existing transfer learning models. It can successfully complete the migration task and accurately recognize the painting defects of known categories and new unknown categories, which is a perfect combination of intelligent algorithms and engineering practice.

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Section: Introductionmentioning

confidence: 99%

A Zero-Shot Image Classification Method of Ship Coating Defects Based on IDATLWGAN

Bu,

Yang,

et al. 2024

Coatings

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Metaheuristic-based ensemble learning: an extensive review of methods and applications

Rezk,

Selim

2024

Neural Comput & Applic

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Ensemble learning has become a cornerstone in various classification and regression tasks, leveraging its robust learning capacity across disciplines. However, the computational time and memory constraints associated with almost all-learners-based ensembles necessitate efficient approaches. Ensemble pruning, a crucial step, involves selecting a subset of base learners to address these limitations. This study underscores the significance of optimization-based methods in ensemble pruning, with a specific focus on metaheuristics as high-level problem-solving techniques. It reviews the intersection of ensemble learning and metaheuristics, specifically in the context of selective ensembles, marking a unique contribution in this direction of research. Through categorizing metaheuristic-based selective ensembles, identifying their frequently used algorithms and software programs, and highlighting their uses across diverse application domains, this research serves as a comprehensive resource for researchers and offers insights into recent developments and applications. Also, by addressing pivotal research gaps, the study identifies exploring selective ensemble techniques for cluster analysis, investigating cutting-edge metaheuristics and hybrid multi-class models, and optimizing ensemble size as well as hyper-parameters within metaheuristic iterations as prospective research directions. These directions offer a robust roadmap for advancing the understanding and application of metaheuristic-based selective ensembles.

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Visual Ship Image Synthesis and Classification Framework Based on Attention-DCGAN

Xiao,

Luo,

et al. 2024

Int J Comput Intell Syst

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To improving ship image generation and classification tasks, a deep convolution generative adversarial network based on attention mechanism (ADCGAN) model was constructed. The rectified linear unit (ReLU) activation function was adopted, and three Deconv layers and Conv layers were added to both the generator and discriminator. Subsequently, an attention mechanism was added to the generator, while spectral normalization (SN) was added to the discriminator. Mean squared error (MSE) was used as loss function to stabilize the training process. Furthermore, ship classification tasks were performed using the generated ship images by end-to-end training of the classification network, enabling ship data augmentation and co-learning with other tasks. Experimental results on the Ship700 and Seaship7000 datasets demonstrate that the ADCGAN model can generate clear and robust ship images, with PSNR, LIPIPS, MS-SSIM values of 20.279 and 27.523, 0.596 and 0.096, 0.781 and 0.947, respectively. The effectiveness of the proposed method in ship image classification tasks was also verified, providing a data foundation for other collaborative tasks.

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An Image Generation Method of Unbalanced Ship Coating Defects Based on IGASEN-EMWGAN

Cited by 3 publications

References 62 publications

A Zero-Shot Image Classification Method of Ship Coating Defects Based on IDATLWGAN

A Zero-Shot Image Classification Method of Ship Coating Defects Based on IDATLWGAN

Metaheuristic-based ensemble learning: an extensive review of methods and applications

Visual Ship Image Synthesis and Classification Framework Based on Attention-DCGAN

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