AFT: Adaptive Fusion Transformer for Visible and Infrared Images

Chang, Zhihao; Feng, Zhixi; Yang, Sen; Gao, Quanwei

doi:10.1109/tip.2023.3263113

Cited by 26 publications

(2 citation statements)

References 51 publications

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“…It is widely used in many fields, 50–52 such as image classification. 53–55 In this experiment, it was introduced into the Image module to enhance the model's feature extraction capabilities and improve the pressure prediction performance. Fig.…”

Section: Resultsmentioning

confidence: 99%

Air pressure prediction model based on the fusion of laser-induced plasma images and spectra

Ke,

Luo,

et al. 2024

J. Anal. At. Spectrom.

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Section: Resultsmentioning

confidence: 99%

Air pressure prediction model based on the fusion of laser-induced plasma images and spectra

Ke,

Luo,

et al. 2024

J. Anal. At. Spectrom.

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“…The Vision Transformer (ViT) architecture, introduced by Google in 2020, has proven to be an effective deep learning approach for a wide range of visual tasks. It serves as a general-purpose backbone for various downstream tasks, including image classification [ 18 ], object detection [ 19 ], semantic segmentation [ 20 , 21 ], human pose estimation [ 22 ], and image fusion [ 23 , 24 ]. Unlike traditional convolutional neural networks (CNNs), ViT eliminates the need for hand-crafted feature extraction and data augmentation, which can be time-consuming.…”

Section: Related Workmentioning

confidence: 99%

PCB Defect Detection via Local Detail and Global Dependency Information

Feng,

Cai

2023

Sensors

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Due to the impact of the production environment, there may be quality issues on the surface of printed circuit boards (PCBs), which could result in significant economic losses during the application process. As a result, PCB surface defect detection has become an essential step for managing PCB production quality. With the continuous advancement of PCB production technology, defects on PCBs now exhibit characteristics such as small areas and diverse styles. Utilizing global information plays a crucial role in detecting these small and variable defects. To address this challenge, we propose a novel defect detection framework named Defect Detection TRansformer (DDTR), which combines convolutional neural networks (CNNs) and transformer architectures. In the backbone, we employ the Residual Swin Transformer (ResSwinT) to extract both local detail information using ResNet and global dependency information through the Swin Transformer. This approach allows us to capture multi-scale features and enhance feature expression capabilities.In the neck of the network, we introduce spatial and channel multi-head self-attention (SCSA), enabling the network to focus on advantageous features in different dimensions. Moving to the head, we employ multiple cascaded detectors and classifiers to further improve defect detection accuracy. We conducted extensive experiments on the PKU-Market-PCB and DeepPCB datasets. Comparing our proposed DDTR framework with existing common methods, we achieved the highest F1-score and produced the most informative visualization results. Lastly, ablation experiments were performed to demonstrate the feasibility of individual modules within the DDTR framework. These experiments confirmed the effectiveness and contributions of our approach.

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FUFusion: Fuzzy Sets Theory for Infrared and Visible Image Fusion

Jie,

Chen,

et al. 2023

Lecture Notes in Computer Science

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AFT: Adaptive Fusion Transformer for Visible and Infrared Images

Cited by 26 publications

References 51 publications

Air pressure prediction model based on the fusion of laser-induced plasma images and spectra

Air pressure prediction model based on the fusion of laser-induced plasma images and spectra

PCB Defect Detection via Local Detail and Global Dependency Information

FUFusion: Fuzzy Sets Theory for Infrared and Visible Image Fusion

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