In an era of rapid technological advancements, keeping up with the iterative updates of factory technology in Intelligent manufacturing presents a daunting challenge. Against this backdrop, this paper investigates the...
In an era of rapid technological advancements, keeping up with the iterative updates of factory technology in Intelligent manufacturing presents a daunting challenge. Against this backdrop, this paper investigates the performance of the ECA-YOLOv5n model, a novel approach for appearance quality detection in air conditioner external units. Building upon an open-source dataset, ECA-YOLOv5n model integrates the ECA-Net attention mechanism into the YOLOv5-nano model. ECA-Net effectively captures channel-wise dependencies and studies discriminative features, allowing for seamless integration into the convolutional neural networks of the YOLOv5 architecture without the need for extensive modifications. Furthermore, the efficient channel attention mechanism of ECA-Net demands fewer parameters and computations in comparison to other attention mechanisms, conferring a significant advantage in terms of computational efficiency. Experimental results show that the newly proposed model achieves approximately 98% precision, 99% recall, and 99% mAP on the dataset. The ECA-YOLOv5n model reduces storage usage and minorly increases detection speed in comparison to other YOLO models.
In this paper, we propose a novel method, namely boundary feature pyramid network (BFP-Net), which can effectively segment targets with blurred boundaries. Specifically, we first propose a global feature fusion module...
In this paper, we propose a novel method, namely boundary feature pyramid network (BFP-Net), which can effectively segment targets with blurred boundaries. Specifically, we first propose a global feature fusion module (GFFM) at the top of BFP-Net to fuse feature maps within different scales. It can learn more global region localization of the target and help the learning of boundaries more effectively. Then, we propose a series of boundary enhancement modules (BEMs) at the decoder to effectively extract and integrate boundary information during the upsampling process, thereby enhancing the ability to capture fine details (such as the boundaries). Furthermore, we introduce a boundary-enhanced composite loss function to effectively segment both the regions and their boundaries within different scales. Finally, extensive experiments on two widely-used datasets demonstrate that BFP-Net is more effective in fusing contextual information and guiding feature map boundaries compared to previous competitive methods.
With the popularity of encryption protocols, machine learning (ML)-based traffic analysis technologies have attracted widespread attention. To adapt to modern high-speed bandwidth, recent research is dedicated to adva...
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Link prediction has achieved great success on ubiquitous graph-based applications, which usually contain multiple types of connections. The heterogeneity of networks introduces complexities in two aspects: representat...
Link prediction has achieved great success on ubiquitous graph-based applications, which usually contain multiple types of connections. The heterogeneity of networks introduces complexities in two aspects: representation of multiple types of links, and incorporation of domain knowledge. In this paper, we construct Markov logic network (MLN) to preserve he logical information of graphs for link prediction tasks. By using the graph neural network, we propose a novel inference method in MLNs to guarantee the scalability of link prediction. Experimental results on various datasets show that our proposed method outperforms the state-of-the-art competitors on precision and efficiency.
Recent breakthroughs in quantum hardware are creating opportunities for its use in many applications. However, quantum software engineering is still in its infancy with many challenges, especially dealing with the div...
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The computational inefficiency of spiking neural networks (SNNs) is primarily due to the sequential updates of membrane potential, which becomes more pronounced during extended encoding periods compared to artificial ...
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With the increasing sophistication of musical instruments and the importance of precise tuning, a novel and efficient approach to instrument calibration is of significant necessity. This paper presents VGRISys, an inn...
Due to the scarcity of point cloud datasets in a specific domain, utilizing generative model approaches becomes essential for data augmentation. Diffusion models have demonstrated impressive capabilities in data gener...
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Existing prompt learning methods have shown certain capabilities in Out-of-Distribution (OOD) detection, but the lack of OOD images in the target dataset in their training can lead to mismatches between OOD images and...
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This study explores the effectiveness of Convolutional Neural Networks (CNNs) in automatically classifying skin cancer for e-health applications. The trained model showcases impressive performance by leveraging the HA...
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