YOLO V8智能城市国外探测方法--译【杂文】
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An improved fire detection approach based on YOLO-v8 for smart cities
Fatma M Talaat, Hanaa ZainEldin
Neural Computing and Applications 35 (28), 20939-20954, 2023
Fires in smart cities can have devastating consequences, causing damage to property, and endangering the lives of citizens. Traditional fire detection methods have limitations in terms of accuracy and speed, making it challenging to detect fires in real time. This paper proposes an improved fire detection approach for smart cities based on the YOLOv8 algorithm, called the smart fire detection system (SFDS), which leverages the strengths of deep learning to detect fire-specific features in real time. The SFDS approach has the potential to improve the accuracy of fire detection, reduce false alarms, and be cost-effective compared to traditional fire detection methods. It can also be extended to detect other objects of interest in smart cities, such as gas leaks or flooding. The proposed framework for a smart city consists of four primary layers: (i) Application layer, (ii) Fog layer, (iii) Cloud layer, and (iv) IoT layer. The proposed algorithm utilizes Fog and Cloud computing, along with the IoT layer, to collect and process data in real time, enabling faster response times and reducing the risk of damage to property and human life. The SFDS achieved state-of-the-art performance in terms of both precision and recall, with a high precision rate of 97.1% for all classes. The proposed approach has several potential applications, including fire safety management in public areas, forest fire monitoring, and intelligent security systems.
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被引用次数：18
所有 4 个版本
YOLO-v1 to YOLO-v8, the Rise of YOLO and Its Complementary Nature toward Digital Manufacturing and Industrial Defect Detection
Muhammad Hussain
Machines 11 (7), 677, 2023
Since its inception in 2015, the YOLO (You Only Look Once) variant of object detectors has rapidly grown, with the latest release of YOLO-v8 in January 2023. YOLO variants are underpinned by the principle of real-time and high-classification performance, based on limited but efficient computational parameters. This principle has been found within the DNA of all YOLO variants with increasing intensity, as the variants evolve addressing the requirements of automated quality inspection within the industrial surface defect detection domain, such as the need for fast detection, high accuracy, and deployment onto constrained edge devices. This paper is the first to provide an in-depth review of the YOLO evolution from the original YOLO to the recent release (YOLO-v8) from the perspective of industrial manufacturing. The review explores the key architectural advancements proposed at each iteration, followed by examples of industrial deployment for surface defect detection endorsing its compatibility with industrial requirements.
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An improved fire detection approach based on YOLO-v8 for smart cities
FM Talaat, H ZainEldin - Neural Computing and Applications, 2023 - Springer
… YOLO) algorithm is one such CNN-based object detection framework that has been widely used in computer vision applications. The latest YOLO v8 … system based on YOLO v8 for smart …
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YOLO-v1 to YOLO-v8, the Rise of YOLO and Its Complementary Nature toward Digital Manufacturing and Industrial Defect Detection
M Hussain - Machines, 2023 - mdpi.com
… This principle has been found within the DNA of all YOLO variants with increasing intensity, … review of the YOLO evolution from the original YOLO to the recent release (YOLO-v8) from …
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Segmentation and Phenotype Calculation of Rapeseed Pods Based on YOLO v8 and Mask R-Convolution Neural Networks
N Wang, H Liu, Y Li, W Zhou, M Ding - Plants, 2023 - mdpi.com
… We utilized the identical dataset with YOLO v8 and performed tasks akin to those in Section 3.1 of our image processing work. The rapeseed pod instance segmentation project was …
High-Speed Drone Detection Based On Yolo-V8
JH Kim, N Kim, CS Won - ICASSP 2023-2023 IEEE …, 2023 - ieeexplore.ieee.org
… version of YOLO-V8. We applied a multi-scale image fusion to the M-model of YOLO-V8, enabling us to … Additionally, we added a P2 layer to YOLOV8 and extensively used copy & paste …
A novel method for real-time object-based copy-move tampering localization in videos using fine-tuned YOLO V8
A Kashyap - Forensic Science International: Digital Investigation, 2024 - Elsevier
… in videos, so to train the YOLO model. First, we need to … recognition tasks in YOLO format for training the YOLO V8. Table 1 … version of the YOLO V8 as well as the original YOLO V8. The …
Enhancing face recognition accuracy through integration of yolo v8 and deep learning: A custom recognition model approach
MJA Daasan, MHIB Ishak - Asia Simulation Conference, 2023 - Springer
… In this report, python, Ultralytics and YOLO v8 had been used to recognize faces of 7 different people by combining YOLO and facial recognition, we have discovered that the YOLO …
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A Comprehensive Review of YOLO Architectures in Computer Vision: From YOLOv1 to YOLOv8 and YOLO-NAS
J Terven, DM Córdova-Esparza… - Machine Learning and …, 2023 - mdpi.com
… We present a comprehensive analysis of YOLO’s evolution, … the original YOLO up to YOLOv8, YOLO-NAS, and YOLO with … 16 YOLO versions, ranging from the original YOLO model to …
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Yolo based real-time human detection for smart video surveillance at the edge
HH Nguyen, TN Ta, NC Nguyen… - 2020 IEEE Eighth …, 2021 - ieeexplore.ieee.org
… On the human COCO test dataset, our trained model outperforms the performance of the Tiny-YOLO versions. Additionally, compare to the SSD based L-CNN method, our algorithm …
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Lightweight You Only Look Once v8: An Upgraded You Only Look Once v8 Algorithm for Small Object Identification in Unmanned Aerial Vehicle Images
Z Huangfu, S Li - Applied Sciences, 2023 - mdpi.com
… -YOLO v8 in this work outperforms the baseline model YOLO … the LW-YOLO v8 model and the baseline model YOLO v8n. … can be seen that the LW-YOLO v8 model in this work detects …
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Object detection model-based quality inspection using a deep CNN
M Chetoui, MA Akhloufi - … on Quality Control by Artificial Vision, 2023 - spiedigitallibrary.org
… YOLO v8 enhance images during training. At every … YOLO v8 performance in several cases of detection in the field of quality control. We provide a complete comparison of all YOLO v8 …
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一种基于YOLO-v8的智能城市火灾探测方法
法特玛·塔拉特
神经计算与应用35(28),20939-20954,2023
智慧城市的火灾可能会造成毁灭性的后果，造成财产损坏，并危及公民的生命。传统的火灾探测方法在准确性和速度方面存在局限性，这使得实时探测火灾具有挑战性。本文提出了一种基于YOLOv8算法的智能城市火灾检测方法，称为智能火灾检测系统（SFDS），它利用深度学习的优势实时检测火灾特定特征。与传统的火灾探测方法相比，SFDS方法具有提高火灾探测准确性、减少误报和成本效益的潜力。它还可以扩展到检测智能城市中其他感兴趣的对象，例如气体泄漏或洪水。智慧城市的拟议框架由四个主要层组成：（i）应用层，（ii）雾层，（iii）云层和（iv）物联网层。该算法利用雾和云计算以及物联网层，实时收集和处理数据，从而实现更快的响应时间，并降低对财产和人类生命造成损害的风险。SFDS在准确率和召回率方面都达到了最先进的水平，所有类别的准确率高达97.1%。提出的方法有几个潜在的应用，包括公共区域的消防安全管理，森林火灾监测和智能安全系统。
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被引用次数：18
所有 4 个版本
YOLO-v1到YOLO-v8,YOLO的崛起及其对数字化制造和工业缺陷检测的互补性
穆罕默德侯赛因
机器11(7),677,2023
自2015年成立以来，YOLO（你只看一次）物体探测器的变体迅速增长，最新版本的YOLO-v8于2023年1月发布。YOLO变体的基础是基于有限但有效的计算参数的实时和高分类性能原则。这一原理已经在所有YOLO变体的DNA中发现，强度越来越大，因为变体不断发展，以满足工业表面缺陷检测领域中自动化质量检验的要求，例如对快速检测，高精度和部署到受限边缘设备的需求。本文首次从工业制造的角度对YOLO从最初的YOLO到最近发布的YOLO版本（YOLO-v8）的演变进行了深入的回顾。审查探讨了在每个迭代中提出的关键架构进步，随后是用于表面缺陷检测的工业部署示例，支持其与工业要求的兼容性。
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被引用次数：27
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一种基于YOLO-v8的智能城市火灾探测方法
FM Talaat,H ZainEldin-神经计算和应用，2023-Springer
YOLO)算法就是一种基于CNN的物体检测框架，在计算机视觉应用中得到了广泛的应用。最新的YOLO v8...基于YOLO v8的智能...
保存 被引用次数：18
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YOLO-v1到YOLO-v8,YOLO的崛起及其对数字化制造和工业缺陷检测的互补性
M侯赛因-机器，2023-mdpi.com
...这一原则已经在所有YOLO变体的DNA中发现，强度越来越大，...回顾了YOLO从原始YOLO到最近发布的YOLO版本（YOLO-v8）的演变...
保存 被引用次数：27 相关文章
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基于YOLO v8和Mask R卷积神经网络的油菜豆荚分割和表型计算
N王，H刘，Y李，W周，M丁-植物，2023-mdpi.com
我们使用YOLO v8相同的数据集，并执行类似于我们图像处理工作第3.1节中的任务。油菜豆荚实例分割项目是...
保存 被引用次数：1
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基于Yolo-V8的高速无人机检测
JH Kim,N Kim,CS Won-ICASSP 2023-2023 IEEE...,2023-ieeexplore.ieee.org
YOLO-V8版本。我们在YOLO-V8的M模型中应用了多尺度图像融合，使我们能够。此外，我们在YOLOV8中添加了P2层，并广泛使用了复制粘贴。
保存 被引用次数：11 相关文章
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一种基于对象的实时复制-移动篡改视频定位的新方法
A Kashyap-法医科学国际：数字调查，2024-Elsevier
...在视频中，所以要训练YOLO模型。首先，我们需要...以YOLO格式训练YOLO V8的识别任务。表1...版本的YOLO V8以及原始的YOLO V8.