An interactive deep model combined with Retinex for low-light visible and infrared image fusion

Wang, Changcheng; Zang, Yongsheng; Zhou, Dongming; Nie, Rencan; Mei, Jiatian

doi:10.1007/s00521-023-08314-5

Cited by 6 publications

(1 citation statement)

References 32 publications

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“…Within the structural diagrams, the function

is introduced to regulate the activation threshold. This function initially conducts global pooling to reduce dimensions across the

plane, followed by two cascaded fully connected layers, an NL module, and ultimately, the application of a shifted sigmoid function to normalize the output value to the range of [−1, 1] [ 44 ]. The detailed schematic diagrams of these three attention modules are shown in Figure 9 .…”

Section: Proposed Methodsmentioning

confidence: 99%

SRE-YOLOv8: An Improved UAV Object Detection Model Utilizing Swin Transformer and RE-FPN

Li,

Zhang,

Shao

et al. 2024

Sensors

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To tackle the intricate challenges associated with the low detection accuracy of images taken by unmanned aerial vehicles (UAVs), arising from the diverse sizes and types of objects coupled with limited feature information, we present the SRE-YOLOv8 as an advanced method. Our method enhances the YOLOv8 object detection algorithm by leveraging the Swin Transformer and a lightweight residual feature pyramid network (RE-FPN) structure. Firstly, we introduce an optimized Swin Transformer module into the backbone network to preserve ample global contextual information during feature extraction and to extract a broader spectrum of features using self-attention mechanisms. Subsequently, we integrate a Residual Feature Augmentation (RFA) module and a lightweight attention mechanism named ECA, thereby transforming the original FPN structure to RE-FPN, intensifying the network’s emphasis on critical features. Additionally, an SOD (small object detection) layer is incorporated to enhance the network’s ability to recognize the spatial information of the model, thus augmenting accuracy in detecting small objects. Finally, we employ a Dynamic Head equipped with multiple attention mechanisms in the object detection head to enhance its performance in identifying low-resolution targets amidst complex backgrounds. Experimental evaluation conducted on the VisDrone2021 dataset reveals a significant advancement, showcasing an impressive 9.2% enhancement over the original YOLOv8 algorithm.

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“…Within the structural diagrams, the function

is introduced to regulate the activation threshold. This function initially conducts global pooling to reduce dimensions across the

Section: Proposed Methodsmentioning

confidence: 99%