MCANet: Multi-channel attention network with multi-color space encoder for underwater image classification

Li, Guohou; Wang, Fangyuan; Zhou, Luping; Jin, Songlin; Xie, Xiwang; Ding, Chang; Pan, Xipeng; Zhang, Weidong

doi:10.1016/j.compeleceng.2023.108724

Cited by 6 publications

(1 citation statement)

References 19 publications

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“…Channel attention has shown significant advantages in many image processing tasks. For example, in image classification tasks, channel attention can help the network to better distinguish feature differences between different categories [ 13 , 14 , 15 , 16 , 17 ]. In a target detection task, channel attention improves the network’s ability to accurately locate and recognize targets [ 18 , 19 , 20 , 21 , 22 ].…”

Section: Introductionmentioning

confidence: 99%

A Novel Strategy for Extracting Richer Semantic Information Based on Fault Detection in Power Transmission Lines

Yan,

Li,

Wang

et al. 2023

Entropy

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With the development of the smart grid, the traditional defect detection methods in transmission lines are gradually shifted to the combination of robots or drones and deep learning technology to realize the automatic detection of defects, avoiding the risks and computational costs of manual detection. Lightweight embedded devices such as drones and robots belong to small devices with limited computational resources, while deep learning mostly relies on deep neural networks with huge computational resources. And semantic features of deep networks are richer, which are also critical for accurately classifying morphologically similar defects for detection, helping to identify differences and classify transmission line components. Therefore, we propose a method to obtain advanced semantic features even in shallow networks. Combined with transfer learning, we change the image features (e.g., position and edge connectivity) under self-supervised learning during pre-training. This allows the pre-trained model to learn potential semantic feature representations rather than relying on low-level features. The pre-trained model then directs a shallow network to extract rich semantic features for downstream tasks. In addition, we introduce a category semantic fusion module (CSFM) to enhance feature fusion by utilizing channel attention to capture global and local information lost during compression and extraction. This module helps to obtain more category semantic information. Our experiments on a self-created transmission line defect dataset show the superiority of modifying low-level image information during pre-training when adjusting the number of network layers and embedding of the CSFM. The strategy demonstrates generalization on the publicly available PASCAL VOC dataset. Finally, compared with state-of-the-art methods on the synthetic fog insulator dataset (SFID), the strategy achieves comparable performance with much smaller network depths.

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Section: Introductionmentioning

confidence: 99%

A Novel Strategy for Extracting Richer Semantic Information Based on Fault Detection in Power Transmission Lines

Yan,

Li,

Wang

et al. 2023

Entropy

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FishAI: Automated hierarchical marine fish image classification with vision transformer

Yang,

Zhou,

Wang

et al. 2024

Engineering Reports

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To address the issues of high demand for efficiently recognizing fish species in marine scientific research, such as impact assessments on biodiversity and monitoring, an automated hierarchical image classification web‐based platform, named FishAI, was developed. Trained with marine fish images collected from the World Register of Marine Species, FishAI used the Vision Transformer (ViT) model, to classify fish. The model considers hierarchy levels, covering 3 classes, 38 orders, 154 families, 438 genera, and 808 species. The FishAI achieved accuracies of 0.975 (Class), 0.798 (Order), 0.743 (Family), 0.638 (Genus), and 0.626 (Species) on test images, respectively, by using the hyperparameter optimization. Comparison between ViT and other baseline backbones proves its superiority by capturing long‐distance dependency. In addition, FishAI yields the top‐5 prediction accuracies of 1.000 (Class), 0.887 (Order), 0.816 (Family), 0.729 (Genus), and 0.727 (Species), respectively. In order to further enhance the practicality of FishAI, the user‐friendly graphic interface (http://www.csbio.sjtu.edu.cn/bioinf/FishAI/) facilitates its easy‐to‐use application. Furthermore, interpretability analysis by Grad‐CAM provides a visual explanation of the highlighted regions on the images for FishAI's prediction among different hierarchies.

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Balanced spatio-spectral feature extraction for hyperspectral and multispectral image fusion

Rajaei,

Abiri,

Helfroush

2024

Computers and Electrical Engineering

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MCANet: Multi-channel attention network with multi-color space encoder for underwater image classification

Cited by 6 publications

References 19 publications

A Novel Strategy for Extracting Richer Semantic Information Based on Fault Detection in Power Transmission Lines

A Novel Strategy for Extracting Richer Semantic Information Based on Fault Detection in Power Transmission Lines

FishAI: Automated hierarchical marine fish image classification with vision transformer

Balanced spatio-spectral feature extraction for hyperspectral and multispectral image fusion

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