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Proceedings of the 8th ieee/ACM international conference on Big Data computing, Applications and Technologies, BDCAT 2021

Proceedings of the 8th IEEE/ACM International Conference on ...

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8th ieee/ACM international conference on Big Data computing, Applications and Technologies, BDCAT 2021

ISBN: (纸本)9781450391641

the proceedings contain 15 papers. the topics discussed include: evaluating serverless architecture for big data enterprise applications;3D object recognition for virtual reality based digital twins;linking user accounts across social media platforms;crowd counting using deep learning in edge devices;multiscale clustering based diffusion representation learning method;a proactive data-parallel framework for machine learning;distributed orchestration of regression models over administrative boundaries;attribute network embedding method based on joint clustering of representation and network;and comparative analysis of pre-trained deep neural networks for vision-based security systems on a novel dataset.

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Deep Learning Model YOLOv5 for Red Chilies Detection from Chilly Crop Images 8

Deep Learning Model YOLOv5 for Red Chilies Detection from Ch...

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8th ieee international conference for Convergence in Technology, I2CT 2023

作者： Ram, Pusala Pawan Venkata Sai Yaswanth, Kaza Venkata Siva Kamepalli, Sujatha Sankar, Bodapati Siva Madupalli, Manpj VFSTR University School of Computing & Informatics Guntur India

ISBN: (纸本)9798350334012

the agriculture industry has been facing a growing challenge with the rising cost of labor in recent years. A large portion of the expenses incurred in agriculture goes towards employing workers. this increase in labor expenses puts farmers under pressure to find ways to reduce costs while still producing at high levels. To tackle this issue, many farmers have turned to technology and automation. Using machines and automated systems to perform tasks that were previously done by humans can help decrease labor costs and increase efficiency, keeping farmers competitive in a continuously changing industry. the usage of technology in agriculture has rapidly increased in various forms, including precision farming, crop monitoring, irrigation systems, and decision-making tools based on data. Additionally, the growth of machine learning and AI has also played a crucial role in modernizing agriculture, with computer vision-based systems being developed for crop detection, classification, and monitoring. this research focuses on creating a red chili detection system using the YOLOv5 deep learning model. this project's objective is to accurately and successfully identify ripened red chilies in real-world images. the proposed approach first applies methods for preparing images to increase the input photos' quality, and then utilizes the YOLOv5 model for object detection. the model was trained using a large dataset of red chili images and evaluated based on performance metrics, including accuracy, speed, and robustness to variations in the input data. the outcomes of our tests show that the suggested technique is more successful than other cutting-edge deep learning models, making it a promising solution for applications in the fields of agricultural automation and food processing. © 2023 ieee.

关键词： Crops

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Centralized Motion-Aware Enhancement for Single object Tracking on Point Clouds 8

Centralized Motion-Aware Enhancement for Single Object Track...

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8th ieee international conference on Cloud computing and Intelligence Systems, CCIS 2022

作者： Wu, Yue Liu, Jiaming Gong, Maoguo Ma, Wenping Miao, Qiguang Xidian University Xi'an710071 China

ISBN: (纸本)9781665477352

3D Single object Tracking (SOT) in LiDAR point clouds has broad application prospects in computer vision, and objects are usually represented by 3D boxes in point clouds. Current methods mostly follow the representation matching-based siamese pattern. However, due to the severe sparse, incomplete shapes of LiDAR point clouds, and the fact that objects in the 3D world do not follow any specific orientation, these are common obstacles to point cloud tracking. In this paper, we propose to represent 3D objects as points, using a key point detector to detect the center of the object and enhance the feature description of the target object, based on a simple and efficient way for more accurate feature comparison. In particular, we introduce a motion-centric paradigm that localizes objects via motion in successive frame transformations. Experimental results demonstrate that our proposed method achieves satisfactory results on both the KITTI and nuScenes benchmarks, achieving a 10% improvement in accuracy compared to state-of-the-art methods. Furthermore, our analysis confirms the effectiveness of each component and shows the great potential of the motion-centric paradigm when combined with representation matching. © 2022 ieee.

关键词： Optical radar

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An Attention-Lightweight YOLOv5 Aerial Dense object Detection Method 8

An Attention-Lightweight YOLOv5 Aerial Dense Object Detectio...

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8th ieee international conference on Cloud computing and Intelligence Systems, CCIS 2022

作者： Xingyu, Xia Weifeng, Liu Zhangming, Peng Hangzhou Dianzi University Hangzhou Zhejiang310018 China Shanxi University of Science and Technology Xian Shanxi710016 China

ISBN: (纸本)9781665477352

the paper proposes an improved YOLOv5-CBG (SX-CBAM, Ghostbottleneck) real-time target detection network for the problem of false detection and high missed detection rate in real-time detection caused by a large number of dense small targets in complex aerial images. the network first adds a hybrid pooling layer to the attention mechanism CBAM (Convolutional Block Attention Module), and integrates the residual path into its structure, and then embeds the improved SX-CBAM into the YOLOv5 detection network to improve its detection performance. Secondly, combined with the lightweight Ghost bottleneck module to reduce the computational cost of the network and ensure the real-time performance of target detection. the results of comparison experiments on the dataset VisDrone2019 show that the mAP50 of YOLOv5-CBG is 5.2% higher and the GFLOPs are reduced by 1.4 compared to YOLOv5, demonstrating that the improved detection network reduces the false detection rate and missed detection rate for dense small targets while satisfying real-time target detection. © 2022 ieee.

关键词： object detection

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Recover the Structure of Monocular Depth Estimation base ViT-CNNs 19

Recover the Structure of Monocular Depth Estimation base ViT...

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2022 ieee SmartWorld, 19th ieee international conference on Ubiquitous Intelligence and computing, 2022 ieee international conference on Autonomous and Trusted Vehicles conference, 22nd ieee international conference on Scalable computing and Communications, 2022 ieee international conference on Digital Twin, 8th ieee international conference on Privacy computing and 2022 ieee international conference on Metaverse, SmartWorld/UIC/ATC/ScalCom/DigitalTwin/PriComp/Metaverse 2022

作者： Yang, Xin Chang, Qingling Liu, Xinglin Wuyi University Faculty of Intelligent Manufacturing Jiangmen China Faculty of Intelligent Manufacturing Jiangmen China

ISBN: (纸本)9798350346558

Monocular depth estimation is a fundamental vision task that is applied to many vision works such as scene reconstruction, object detection, automatic drive, and Augmented Reality AR/Virtual Reality VR. the monocular depth estimation base on Vision Transformer (ViT) has shown remarkable ability. ViT has an excellent capability to model long dependency and global information. However, it shows weak performance in preserving local information. Local information in the depth maps is an important cue for improving depth accuracy and other vision tasks. Furthermore, the local and detailed information is the key to reconstruct the scene and other works. To overcome this flaw and recover structure information (local and detail) in the depth maps, we propose a model with the ViT encoder and Convolutional Neural Networks CNNs sub-network. the CNNs sub-network is used to prove the local information. ViT captured the global information. However, the gap between the CNNs feature and ViT feature is huge. So we propose an exchange fusion strategy to reduce this gap. Furthermore, to enhance the effectiveness of exchange fusion, mixed attention is designed. Finally, to guide the framework to focus on the local feature, a structure loss function that contains the detail term and outline term is proposed. the detail term and outline term guide the model to focus on the non-edge region with a slight gradient and the edge region having a huge gradient respectively. Extensive experiments prove that our depth maps with a richer and clearer structure of objects and scenes than others. Furthermore, ours achieve state-of-the-art in edge accuracy, excellent accuracy of depth on NUY-depth v2 dataset. © 2022 ieee.

关键词： object detection

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YOLOv3-XH: More Accurate object Detection Using Feature Enhancement and Fusion 8

YOLOv3-XH: More Accurate Object Detection Using Feature Enha...

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8th ieee international conference on Computer and Communications, ICCC 2022

作者： Wang, Xiaohui Yin, Zhenyu Fan, Chao Li, Jiong Bi, Zhiying Li, Hui University of Chinese Academy of Sciences Shenyang China Shenyang Institute of Computing Technology Chinese Academy of Sciences Shenyang China Liaoning Key Lab. of Domestic Industrial Control Platform Technology on Basic Hardware and Software Shenyang China

ISBN: (纸本)9781665450515

object detection is an important basis for understanding the high-level semantic information of images. To address the problems of small object accuracy and inaccurate bounding box localization in the YOLOv3, we propose a new object detection method, YOLOv3-XH. In YOLOv3-XH, we make three improvements to YOLOv3. Firstly, we propose the Enhanced Feature Pyramid Network (EFPN), which fuses Path Aggregation Network (PAN) and Feature Pyramid Network (FPN) and helps extract richer feature information and increase the accuracy of small object detection. Secondly, the Multi-Branch Receptive Field (MBRF) is proposed, which adds a branch to RFB for feature extraction and helps to increase the receptive field and enhance feature extraction. Finally, a new loss function is proposed, using GIo U loss as the bounding box loss function, so that the network is optimised in the direction of a higher overlap between the predicted and real boxes. Our approach achieved an average mean accuracy mAP of 83.02% (IoU=O.5) on the PASCAL VOC test set, reaching an improvement of 1.83%. © 2022 ieee.

关键词： object detection

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A Privacy Preserving Video Surveillance System for Trauma Rooms 19

A Privacy Preserving Video Surveillance System for Trauma Ro...

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作者： Ren, Zhengyong Ghazinour, Kambiz Guan, Qiang Bayramzadeh, Sara Yang, Yuxin Kent State University Department of Computer Science United States State University of New York at Canton School of Science Health and Criminal Justice United States Kent State University College of Architecture and Environmental Design United States

ISBN: (纸本)9798350346558

Video surveillance in trauma room can improve the security, patient safety, healthcare standards, and hygiene monitoring, but video surveillance system can cause privacy breach by showing and storing sensitive information of patients and staff. Head blurring can protect privacy, and we can still able to know most actions of people with blurred head. But when postures of people in the videos are complex or when there are many people, using real-time detection program to detect and blur heads is challenging, with a high missed detection rate. OpenPose is a state-of-the-art human body skeletons estimation framework. And we build a head detection and blurring technique based on OpenPose to solve this problem. We first use OpenPose to extract 18 human key skeleton points, and find 5 key skeleton points which are used to describe head. Finally, we apply the image obfuscation techniques to the detected heads. the experiment results show that our proposed techniques can ensure privacy protection and reduces the missed detection rate of the output results. Our experiment results prove that our proposed technique has a high value in privacy protection applications. © 2022 ieee.

关键词： object detection

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An Exploratory Study on the Design of Emergency First Aid Privacy Protection computing System Based on Blockchain 8

An Exploratory Study on the Design of Emergency First Aid Pr...

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8th international conference on Business and Industrial Research, ICBIR 2023

作者： Jian, Xiong Parthasarathy, Rajamohan Huang, Bin Wen SEGi University Faculty of Engineering Built Environment and Information Technology Kota Damansara Malaysia Hainan Medical University Faculty of Engineering Built Environment and Information Technology Kota Damansara Malaysia Center Hainan Medical University Faculty of Modern Education Technology Haikou China

ISBN: (纸本)9798350399646

the COVID-19 epidemic at the end of 2019 is a typical global public health event. It has spread to more than 200 countries around the world. In the face of sudden illness, the emergency department is the first choice for most patients, and it is often the first to grasp the basic situation of patients. In the process of processing the data of the emergency big data center, how to ensure the privacy of patients while using medical data reasonably. this paper mainly takes the data of the emergency big data center as the research object. Put important health data on the block chain to ensure that important data remains traceable throughout the process, cannot be tampered with, multi-point maintenance, unified standards, and realize the formation of emergency block chains, and use important health data based on privacy applications, Realize the historical traceability of data usage. At the same time, the calculation process of privacy calculation can also be uploaded to the chain, which can be used for data storage, auxiliary calculation, etc. © 2023 ieee.

关键词： COVID-19

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LightOcean: A Lightweight And Efficient Network For Real-time UAV Tracking 19

LightOcean: A Lightweight And Efficient Network For Real-tim...

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作者： Chen, Haiyang Wang, Weiqiang Zhang, Xingzhou Zhou, Wei Shi, Weisong Yunnan University Engineering Research Center of Cyberspace Kunming650091 China Yunnan University School of Software Kunming650091 China Institute of Computing Technology Chinese Academy of Sciences Beijing100190 China University of Chinese Academy of Sciences Beijing100190 China University of Delaware Department of Computer and Information Sciences NewarkDE19716 United States

ISBN: (纸本)9798350346558

Siamese-based trackers have significantly advanced in visual object tracking over the past years. However, most of these trackers emphasize tracking accuracy over efficiency, which limits their real-world deployment on edge platforms with widespread applications such as unmanned aerial vehicles (UAVs). In this paper, we propose LightOcean, a lightweight and efficient aerial tracker. Specifically, we design a dynamic template feature update module and a pixel-level cross-correlation module to improve the robustness and adaptability of the tracker without additional model computation. the former allows the tracker to capture additional space-time information during the tracking process to adapt to the appearance changes of the object. the latter utilizes pixel-level cross-correlation and attention mechanisms to generate similarity maps, improving the accuracy of predicted boundaries. By combining the lightweight and efficient backbone network and the prediction head, LightOcean significantly increases the tracking speed while maintaining accuracy. By running three common UAV benchmarks on Jetson Nano, a typical edge-embedded device, LightOcean presents better performance. the tracking speed is 4x faster than the state-of-the-art tracker, Ocean, while the energy consumption and memory usage are reduced by 80% and 12%. What's more, the accuracy remained stable, and the precision of LightOcean is 78.6%, which is 2.7% higher than Ocean. © 2022 ieee.

关键词： Antennas

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Proceedings - 2022 ieee SmartWorld, Ubiquitous Intelligence and computing, Autonomous and Trusted Vehicles, Scalable computing and Communications, Digital Twin, Privacy computing, Metaverse, SmartWorld/UIC/ATC/ScalCom/DigitalTwin/PriComp/Metaverse 2022

Proceedings - 2022 IEEE SmartWorld, Ubiquitous Intelligence ...

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ISBN: (纸本)9798350346558

the proceedings contain 339 papers. the topics discussed include: detecting and classifying adversarial examples based on DCT transform;nonintrusive measurement on temporal and spatial features of microservice inferences;egocentric hand-object interaction detection;continuously monitoring optimal routes with collective spatial keywords on road networks;edge-aided cellular learning automata for energy-efficient sensor self-relocation in mobile WSNs;jamming-resilient over-the-air computation for federated learning in edge intelligence;an exact potential game-based end-edge-cloud collaborative task offloading approach;efficient federated learning with adaptive channel pruning for edge devices;data-driven vehicular mobility model introducing with kinetic car-following models for traffic simulation;a fault-tolerant and real-time framework for efficient resilience in edge inference systems;few-shot node classification on attributed networks based on prototypical network;and energy-efficient online node cooperation strategy for hierarchical federated learning.

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