In the rapidly advancing field of intelligent transportation systems, integrating artificial intelligence (AI) with edge computing presents a promising way to enhance the safety and efficiency of the Internet of Vehic...
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In the rapidly advancing field of intelligent transportation systems, integrating artificial intelligence (AI) with edge computing presents a promising way to enhance the safety and efficiency of the Internet of Vehicles (IoV). This study explores and presents a deep learning-based object detection model within an edge computing framework which aims to facilitate real time object detection in self driving cars. Using an urban traffic scenarios-based dataset, our research shows the ability of the model to accurately detect and classify various objects important for autonomous driving. The YOLOv8 model is used in this work due to its optimal balance between accuracy and computational efficiency. This model has also demonstrated its worth by achieving good performance results, including an average precision of 0.79, a recall of 0.62, and an F1-score of 0.69. The results are demonstrated by a detailed confusion matrix, highlighting the model’s effectiveness in complex driving environments and underscoring its reliability for in-vehicle deployment. By implementing AI directly on edge devices within vehicles, our approach might be helpful in significantly reducing latency, boosting decision-making speed, and enhancing data privacy by minimising dependence on cloud processing. The findings not only support the model’s capabilities but also illustrate the practical benefits of edge intelligence in autonomous vehicles. These benefits, such as faster decision making and improved data privacy, contribute effectively to the IoV infrastructure. This study marks a substantial step toward recognizing the possibility of AI-enhanced edge computing in driving the next generation of autonomous vehicle technology.
In this paper, we develop a graph neural network (GNN)-assisted bilinear inference approach to enhance the receiver performance of the MIMO system through message passing-based joint channel estimation and data detect...
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Few-shot semantic segmentation (FSS) methods have shown great promise in handling data-scarce scenarios, particularly in medical image segmentation tasks. However, most existing FSS architectures lack sufficient inter...
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This paper presents a new efficient technique for supervised pixel-based texture classification. The proposed scheme first performs a selection process that automatically determines a subset of prototypes that charact...
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ISBN:
(纸本)9781424456536
This paper presents a new efficient technique for supervised pixel-based texture classification. The proposed scheme first performs a selection process that automatically determines a subset of prototypes that characterize each texture class based on the outcome of a multichannel Gabor wavelet filter bank. Then, every image pixel is classified into one of the given texture classes by using a K-NN classifier fed with the prototypes determined previously. The proposed technique is compared to previous texture classifiers by using both Brodatz and real outdoor textured images.
This paper describes a new technique for determining the distance to a planar surface and, at the same time, obtaining a characterization of the surface's material through the use of conventional, low-cost infrare...
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Texture-based pixel classification has been traditionally carried out by applying texture feature extraction methods that belong to a same family (e.g., Gabor filters). However, recent work has shown that such classif...
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Recent advances in the understanding of animal locomotion have proven it to be a key element of many fields in biology, motion science, and robotics. For the analysis of walking animals, high-speed x-ray videography i...
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A wide variety of texture feature extraction methods have been proposed for texture based image classification and segmentation. These methods are typically evaluated over windows of the same size, the latter being us...
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This paper presents and evaluates a pixel-based texture classifier that integrates multiple texture feature extraction methods through a new scheme based on the Kullback J-divergence. Experimental results show that th...
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This paper proposes a pixel-based texture classifier that integrates multiple texture feature extraction methods in order to identify the regions of an input image that belong to a given set of texture patterns. Exper...
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