Journal ArticleDOI
Blockchain-Enabled Cross-Domain Object Detection for Autonomous Driving: A Model Sharing Approach
TLDR
A novel blockchain-enabled model sharing approach is proposed to improve the performance of object detection with cross-domain adaptation for autonomous driving systems using a domain-adaptive you-only-look-once (YOLOv2) model.Abstract:
Object detection for autonomous driving is a huge challenge in the cross-domain adaptation scenario, especially for the time- and resource-consuming task. Distributed deep learning (DDL) has demonstrated a considerably good balance between efficiency and computation complexity. However, the reliability of DDL is low. Moreover, the cost of training data and model is not priced well. In this article, a novel blockchain-enabled model sharing approach is proposed to improve the performance of object detection with cross-domain adaptation for autonomous driving systems. Based on the blockchain and mobile-edge computing (MEC) technology, a domain-adaptive you-only-look-once (YOLOv2) model is trained across nodes, which can reduce significantly the domain discrepancy for different object categories. Furthermore, smart contracts are developed to perform data storage and model sharing tasks efficiently. The reliability of model sharing is ensured with blockchain consensus. We evaluate the proposed method under public data sets. The simulation results demonstrate that the efficiency and reliability of the proposed approach are better than the reference model.read more
Citations
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Journal ArticleDOI
Blockchain and Machine Learning for Communications and Networking Systems
TL;DR: This paper identifies several important aspects of integrating blockchain and ML, including overview, benefits, and applications, and discusses some open issues, challenges, and broader perspectives that need to be addressed to jointly consider blockchain andML for communications and networking systems.
Journal ArticleDOI
A Survey on Multi-Access Edge Computing Applied to Video Streaming: Some Research Issues and Challenges
TL;DR: Focus on video streaming schemes, a comprehensive summary of the state of the art applying MEC to video streaming is surveyed and a taxonomy of MEC enabled video streaming applications is classified.
Journal ArticleDOI
Near-Online Tracking With Co-Occurrence Constraints in Blockchain-Based Edge Computing
TL;DR: A novel blockchain-based near-online framework called co-occurrence constraints tracklet tracker (CoCTs) is proposed for cross-camera tracking that inherits the advantages of the blockchain technology in sharing information.
Journal ArticleDOI
Blockchain-Enabled Internet of Vehicles With Cooperative Positioning: A Deep Neural Network Approach
TL;DR: A self-positioning correction scheme for the intelligent vehicles is proposed to improve their positioning accuracy, and a multi-intelligent vehicle positioning error sharing model to reduce GPS positioning error of common vehicles (CoVs) in the same segment or area.
Journal ArticleDOI
Enabling Secure Authentication in Industrial IoT With Transfer Learning Empowered Blockchain
TL;DR: Experimental results show that the proposed ATLB not only provides accurate authentications for IIoT applications but also achieves high throughput and low latency.
References
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Book ChapterDOI
Microsoft COCO: Common Objects in Context
Tsung-Yi Lin,Michael Maire,Serge Belongie,James Hays,Pietro Perona,Deva Ramanan,Piotr Dollár,C. Lawrence Zitnick +7 more
TL;DR: A new dataset with the goal of advancing the state-of-the-art in object recognition by placing the question of object recognition in the context of the broader question of scene understanding by gathering images of complex everyday scenes containing common objects in their natural context.
Proceedings ArticleDOI
You Only Look Once: Unified, Real-Time Object Detection
TL;DR: Compared to state-of-the-art detection systems, YOLO makes more localization errors but is less likely to predict false positives on background, and outperforms other detection methods, including DPM and R-CNN, when generalizing from natural images to other domains like artwork.
Posted Content
Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
TL;DR: Faster R-CNN as discussed by the authors proposes a Region Proposal Network (RPN) to generate high-quality region proposals, which are used by Fast R-NN for detection.
Proceedings ArticleDOI
Rich Feature Hierarchies for Accurate Object Detection and Semantic Segmentation
TL;DR: RCNN as discussed by the authors combines CNNs with bottom-up region proposals to localize and segment objects, and when labeled training data is scarce, supervised pre-training for an auxiliary task, followed by domain-specific fine-tuning, yields a significant performance boost.
Book ChapterDOI
SSD: Single Shot MultiBox Detector
Wei Liu,Dragomir Anguelov,Dumitru Erhan,Christian Szegedy,Scott Reed,Cheng-Yang Fu,Alexander C. Berg +6 more
TL;DR: The approach, named SSD, discretizes the output space of bounding boxes into a set of default boxes over different aspect ratios and scales per feature map location, which makes SSD easy to train and straightforward to integrate into systems that require a detection component.
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