A novel FPN network architecture, which designed to modify and improve the performance of the original YOLOv8 mode to overcome the challenges associated with diminished detection accuracy and sluggish wildfire smoke d...
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This article presents LoRaDIP, a novel low-light image enhancement (LLIE) model based on deep image priors (DIPs). While DIP-based enhancement models are known for their zero-shot learning, their expensive computation...
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1 Introduction The Internet of Things(IoT)has facilitated the development of numerous fields in our ***,some equipment in IoT environment lacks sufficient storage and data processing capabilities[1].A feasible strateg...
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1 Introduction The Internet of Things(IoT)has facilitated the development of numerous fields in our ***,some equipment in IoT environment lacks sufficient storage and data processing capabilities[1].A feasible strategy is to leverage the powerful computing capabilities of cloud servers to process the data within the IoT devices.
Quality assessment is a key problem to be resolved in image processing. Few research works have been designed to analyze the quality of images using different techniques. However, the accuracy involved during the proc...
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Emerging technologies of Agriculture 4.0 such as the Internet of Things (IoT), Cloud Computing, Artificial Intelligence (AI), and 5G network services are being rapidly deployed to address smart farming implementation-...
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Visible-infrared object detection has attracted increasing attention recently due to its superior performance and cost-efficiency. Most existing methods focus on the detection of strictly-aligned data, significantly l...
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Visible-infrared object detection has attracted increasing attention recently due to its superior performance and cost-efficiency. Most existing methods focus on the detection of strictly-aligned data, significantly limiting its practical applications. Although several researchers have attempted to explore weakly-aligned visible-infrared object detection, they are limited to small translational deviations and suffer from a low detection speed. This paper first explores non-aligned visibleinfrared object detection with complex deviations in translation, scaling, and rotation, and proposes a fast one-stage detector YOLO-Adaptor, which introduces a lightweight multi-modal adaptor to simultaneously predict alignment parameters and confidence weights between modalities. The adaptor adopts a feature-level alignment during the feature extraction process, ensuring high alignment efficiency. Moreover, we introduce a feature contrastive learning loss to guide the alignment learning of the adaptor, aiming to reduce the representation gap between the two modalities in hyperbolic space to implement feature spatial and distributional consistency. Extensive experiments are conducted on three datasets, including one weakly-aligned and two non-aligned datasets, and the experimental results demonstrate that YOLO-Adaptor could achieve significant performance improvements in terms of speed and accuracy IEEE
Researchers have recently created several deep learning strategies for various tasks, and facial recognition has made remarkable progress in employing these techniques. Face recognition is a noncontact, nonobligatory,...
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Researchers have recently created several deep learning strategies for various tasks, and facial recognition has made remarkable progress in employing these techniques. Face recognition is a noncontact, nonobligatory, acceptable, and harmonious biometric recognition method with a promising national and social security future. The purpose of this paper is to improve the existing face recognition algorithm, investigate extensive data-driven face recognition methods, and propose a unique automated face recognition methodology based on generative adversarial networks (GANs) and the center symmetric multivariable local binary pattern (CS-MLBP). To begin, this paper employs the center symmetric multivariant local binary pattern (CS-MLBP) algorithm to extract the texture features of the face, addressing the issue that C2DPCA (column-based two-dimensional principle component analysis) does an excellent job of removing the global characteristics of the face but struggles to process the local features of the face under large samples. The extracted texture features are combined with the international features retrieved using C2DPCA to generate a multifeatured face. The proposed method, GAN-CS-MLBP, syndicates the power of GAN with the robustness of CS-MLBP, resulting in an accurate and efficient face recognition system. Deep learning algorithms, mainly neural networks, automatically extract discriminative properties from facial images. The learned features capture low-level information and high-level meanings, permitting the model to distinguish among dissimilar persons more successfully. To assess the proposed technique’s GAN-CS-MLBP performance, extensive experiments are performed on benchmark face recognition datasets such as LFW, YTF, and CASIA-WebFace. Giving to the findings, our method exceeds state-of-the-art facial recognition systems in terms of recognition accuracy and resilience. The proposed automatic face recognition system GAN-CS-MLBP provides a solid basis for a
Reinforcement learning (RL) and imitation learning (IL) are quite two useful machine learning techniques that were shown to be potential in enhancing navigation performance. Basically, both of these methods try to fin...
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With the advent of the fourth industrial revolution, data ushered in explosive growth. Federated learning can protect users’ privacy and raw data from being known by third parties. Its client data is only trained loc...
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The success of vision transformer demonstrates that the transformer structure is also suitable for various vision tasks, including high-level classification tasks and low-level dense prediction tasks. Salient object d...
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