Identification and Classification of Aluminum Scrap Grades Based on the Resnet18 Model

Huang, Bo; Liu, Jianhong; Zhang, Qian; Liu, Kang; Li, Kun; Liao, Xinyu

doi:10.3390/app122111133

Cited by 10 publications

(6 citation statements)

References 20 publications

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“…These residual blocks allow for information to be transmitted across layers via "skip connections", effectively solving the vanishing gradient problem in deep networks. ResNet18 has been widely applied in various image recognition tasks, including image classification, object detection and image segmentation [34]. Compared with ResNet18, GhostNet significantly reduces the computational demands and model size using an efficient method of feature map generation.…”

Section: Methodsmentioning

confidence: 99%

Feature Extraction and Diagnosis of Periodic Transient Impact Faults Based on a Fast Average Kurtogram–GhostNet Method

Jiang,

Zhao,

Zang

et al. 2024

Processes

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This paper proposes an improved fault diagnosis algorithm that combines a modified fast kurtogram (FK) method with the lightweight convolutional neural network GhostNet. The FK algorithm can adaptively select resonance demodulation bands for envelope demodulation to extract fault features, but it may be disturbed by non-Gaussian noise. Hence, the fast average kurtogram (FAK) method based on sub-band averaging was introduced. This method effectively weakens the impact of pulse noise on the kurtosis graph by splitting the signal into equal-length sub-signals and calculating the average kurtosis value of all sub-signal filters. Simultaneously, to fully utilize the advantages of deep learning technology in feature extraction and classification, this study used the FAK to convert vibration signals from one-dimensional to two-dimensional kurtosis graphs as the input for the GhostNet model. This combination not only achieved accurate fault diagnosis and classification but also showed significant advantages in processing efficiency and resource utilization. The experimental results indicate that the algorithm excelled in extracting features and diagnosing periodic transient impact faults, and compared with traditional methods, it exhibited noticeable improvements in computational efficiency and resource management.

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Section: Methodsmentioning

confidence: 99%

Feature Extraction and Diagnosis of Periodic Transient Impact Faults Based on a Fast Average Kurtogram–GhostNet Method

Jiang,

Zhao,

Zang

et al. 2024

Processes

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show abstract

“…ResNet-18 serves as the backbone network and is compared with the CNN network. ResNet-18 introduces a residual structure, which can increase the efficiency of information propagation by skipping connections [ 11 , 12 ]. Figure 4 illustrates the residual structure, and the residual structure satisfies Equation (1) [ 13 ].…”

Section: Methodsmentioning

confidence: 99%

CAT-CBAM-Net: An Automatic Scoring Method for Sow Body Condition Based on CNN and Transformer

Xue,

Sun,

Chen

et al. 2023

Sensors

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Sow body condition scoring has been confirmed as a vital procedure in sow management. A timely and accurate assessment of the body condition of a sow is conducive to determining nutritional supply, and it takes on critical significance in enhancing sow reproductive performance. Manual sow body condition scoring methods have been extensively employed in large-scale sow farms, which are time-consuming and labor-intensive. To address the above-mentioned problem, a dual neural network-based automatic scoring method was developed in this study for sow body condition. The developed method aims to enhance the ability to capture local features and global information in sow images by combining CNN and transformer networks. Moreover, it introduces a CBAM module to help the network pay more attention to crucial feature channels while suppressing attention to irrelevant channels. To tackle the problem of imbalanced categories and mislabeling of body condition data, the original loss function was substituted with the optimized focal loss function. As indicated by the model test, the sow body condition classification achieved an average precision of 91.06%, the average recall rate was 91.58%, and the average F1 score reached 91.31%. The comprehensive comparative experimental results suggested that the proposed method yielded optimal performance on this dataset. The method developed in this study is capable of achieving automatic scoring of sow body condition, and it shows broad and promising applications.

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“…The network has an image input size of 224x224. As a result, the network has learned rich feature representations for a wide range of images [24,25].…”

Section: Transfer Learning With Cnnmentioning

confidence: 99%

Breast Cancer Image Classification using Convolutional Neural Networks (CNN) Models

BOUKAACHE,

NASSER EDİNNE,

BOUDJEHEM

2024

International Journal of Informatics and Applied Mathematics

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Breast cancer can progress silently in its early stages and frequently without noticeable symptoms. However, it poses a serious risk to women. It is imperative to recognize this potential health concern to mitigate it early. In the last few years, Convolutional Neural Networks (CNNs) have advanced significantly in their ability to classify images of breast cancer. Their capacity to automatically extract discriminant features from images has enhanced the performances and accuracy of image classification tasks. They outperform state-of-the-art techniques in this area. Furthermore, complicated models that were first learned for certain tasks can be easily adapted to complete new tasks by using transfer-learning approaches. However, deep learning-based categorization techniques could experience overfitting issues, particularly in cases where the dataset is small. The primary goal of this work is to investigate the performances of certain deep learning models to classify breast cancer images and to study the effects of data augmentation techniques, such as image rotation or displacement when utilizing a transfer learning approach. Using certain image datasets, the ResNet18, Resnet50, and VGG16 models demonstrated accuracy improvements, according to our experimental results.

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Identification and Classification of Aluminum Scrap Grades Based on the Resnet18 Model

Cited by 10 publications

References 20 publications

Feature Extraction and Diagnosis of Periodic Transient Impact Faults Based on a Fast Average Kurtogram–GhostNet Method

Feature Extraction and Diagnosis of Periodic Transient Impact Faults Based on a Fast Average Kurtogram–GhostNet Method

CAT-CBAM-Net: An Automatic Scoring Method for Sow Body Condition Based on CNN and Transformer

Breast Cancer Image Classification using Convolutional Neural Networks (CNN) Models

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