Search Results - "feature fusion"

Recognizing hand actions and poses from first-person RGB videos is crucial for applications like human–computer interaction. However, the recognition accuracy is often affected by factors such as occlusion and blurring. In this study, we propose a unified framework for action recognition and hand po...

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By integrating information from RGB images and depth images, the feature perception capability of a defect detection algorithm can be enhanced, making it more robust and reliable in detecting subtle defects on printed circuit boards. On this basis, inspired by the concept of differential amplificati...

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Semantic segmentation plays a critical role in medical image analysis, offering indispensable information for the diagnosis and treatment planning of liver diseases. However, due to the complex anatomical structure of the liver and significant inter-patient variability, the current methods exhibit n...

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Change detection (CD) is detecting and evaluating surface changes by comparing Remote Sensing Images (RSIs) at different times, which is of great significance for environmental protection and urban planning. Due to the need for higher standards in complex scenes, attention-based CD methods have beco...

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Light stress can restrict the normal growth and development of rapeseed seedlings. In order to realize the early diagnosis of light stress in rapeseed seedlings, a 21 d experiment was conducted on rapeseed seedlings of two leaves and one heart stage using hyperspectral imaging technology. After prep...

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Small object detection presents significant challenges in computer vision, often affected by factors such as low resolution, dense object distribution, and complex backgrounds, which can lead to false positives or missed detections. In this paper, we introduce SRM-YOLO, a novel small object detectio...

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High-precision cloud detection is crucial for the interpretation of remote sensing images. Although deep learning-based methods have achieved remarkable results, challenges remain in the fine-grained segmentation of cloud boundaries and the detection of thin clouds over complex land surfaces. To thi...

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Clam farms are typically located in remote areas with limited computational resources, making it challenging to deploy traditional deep learning-based object detection methods due to their large model size and high computational demands. To address this issue, this paper proposes a lightweight detec...

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Given the far-reaching impact of the gold price on global financial markets, accurately predicting the gold price has become essential, with machine learning methods emerging as a prominent tool to tackle this challenge. Nonetheless, traditional single prediction models usually suffer from limited p...

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To address the challenges of leaf–branch occlusion, fruit mutual occlusion, complex background interference, and scale variations in peach detection within complex orchard environments, this study proposes an improved YOLOv11n-based peach detection method named SDA-YOLO. First, in the backbone netwo...

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Within the field of remote sensing, Unmanned Aerial Vehicle (UAV) infrared object detection plays a pivotal role, especially in complex environments. However, existing methods face challenges such as insufficient accuracy or low computational efficiency, particularly in the detection of small object...

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Citrus Huanglongbing (HLB) is one of the most devastating diseases in the global citrus industry, but its early detection under complex field conditions remains a major challenge. Existing methods often suffer from insufficient dataset diversity and poor generalization, and struggle to accurately de...

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Accurate detection of slope cracks plays a crucial role in early landslide disaster warning; however, traditional approaches often struggle to identify fine and irregular cracks. This study introduces a novel deep learning model, Crack-Net, which leverages a multi-modal feature fusion mechanism and...

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The intricate and complex tumor cell morphology in breast pathology images is a key factor for tumor classification. This paper proposes a lightweight breast tumor classification model with multi-frequency feature fusion (LMFM) to tackle the problem of inadequate feature extraction and poor classifi...

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IntroductionDrug combination therapy represents a promising strategy for addressing complex diseases, offering the potential for improved efficacy while mitigating safety concerns. However, conventional wet-lab experimentation for identifying optimal drug combinations is resource-intensive due to th...

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Change detection in very-high-resolution remote sensing images has gained significant attention, particularly with the rise of deep learning techniques such as convolutional neural networks and Transformers. The Mamba structure, successful in computer vision, has been applied to this domain, enhanci...

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Bird monitoring and protection are essential for maintaining biodiversity, and fine-grained bird classification has become a key focus in this field. Audio-visual modalities provide critical cues for this task, but robust feature extraction and efficient fusion remain major challenges. We introduce...

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Due to the complex sea background and the different sizes and shapes of ships, the detection of ship targets has the problems of low detection rate, high false detection rate and high missed detection rate. To solve this problem, this paper proposes a multi-feature weighted fusion and PCA-SVM model...

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Deep neural network fusion approaches utilizing multimodal remote sensing are essential for crop mapping. However, challenges such as insufficient spatiotemporal feature extraction and ineffective fusion strategies still exist, leading to a decrease in mapping accuracy and robustness when these appr...

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The research of small target detection is of great significance for the optimization and improvement of tasks such as small face recognition and remote sensing image detection. However, the small target in the image occupies fewer pixels, lower resolution, unobvious feature information, resulting in...

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