Accurate image segmentation of skin lesions is crucial for the detection and treatment of skin cancer. Based on the modern state space model Mamba, a novel hybrid CNN-Mamba network (BEFNet) is proposed. Specifically, ...
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With the rapid development of the internet, technology, and social media platforms, an increasing number of people are choosing to express their opinions on their social media accounts. This paper aims to address publ...
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Deep neural networks (DNNs) have become prevalent across various domains. However, recent research has revealed their vulnerability to data poisoning attacks, where adversaries inject poisoned data to compromise the u...
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With the rapid development of autonomous Vehicls technology, the detection of surrounding pedestrians, vehicles, Cyclists and other targets by autonomous vehicles is an indispensable technology, especially for pedestr...
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ISBN:
(纸本)9798350363043
With the rapid development of autonomous Vehicls technology, the detection of surrounding pedestrians, vehicles, Cyclists and other targets by autonomous vehicles is an indispensable technology, especially for pedestrian detection. Therefore, there aremore and more related algorithmms and network models based on target recognition. In recent years, many scholars have stagnated in the process of discovering new algorithms and models and have rarely improved (such as SECOND, PointRCNN, PointPillars, etc.) These classic models, due to the different configuration environments of these model codes and the need to redownload each time you want to run a new model, are very time consuming and energy consuming. In order to solve these difficulties, we chose to use the OpenPCDet target detection framework to improve these models. This framework integrates all the above original object detection models to facilitate us to improve and compare the indicators between the models, and in the comparison of the results of the original model built in the OpenPCDet framework, it is found that the PointPillars modelusing 3D single-stage object detection is the most suitable for autonomous Vehicles. The recognition speed of the original PointPillars for vehicles, pedestrians and other objects can fully meet the use of autonomous Vehicles technology, but the accuracy of object recognition, especially in pedestrian detection, needs to be improved. In this regard, we propose a SelfAttention-PointPillars model. Based on the architecture of the PointPillars model and the idea of self-attention, we use our own pillar amount and modify the original backbone structure into our own self-attention network to improve the accuracy of identifying target pedestrians. We also improved the original L1 loss function into a faster weighted L2 function and we also replaced the activation function with the more efficient LeakyRelu function. Therefore, this paper mainly introduces the OpenPCDet target detect
The rapid evolution of the Internet and technological advancements have revolutionized the retail landscape, giving rise to e-commerce as a dominant force in the industry. In response to the increasing demand for onli...
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The task of image caption generation aims to automatically produce natural language descriptions that match the content of images, integrating the fields of machine vision and natural language processing, which holds ...
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The tech industry craves DevOps talent - no surprise there. But the skills gap persists, with outdated curricula leaving too many grads unprepared for the realities of modern software development. This study takes a h...
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Cloud robotics is an innovative field that leverages cloud technologies-including cloud computing (CC), cloud storage, deep learning, big data, and the Internet of Things to augment the capabilities of robotics. This ...
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As a new federated learning(FL) paradigm, clustered federated learning (CFL) could effectively address the issue of model training accuracy loss due to different data distribution in FL. However, the introduction of t...
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Federated Learning (FL) has gained prominence for collaborative training across multiple devices without data sharing. However, traditional FL overlooks two crucial aspects: collaborative fairness and privacy protecti...
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