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检索条件"主题词=Detection Algorithms"

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YOLOv5s-FSDA: Fire and Smoke detection Algorithm Based on Improved YOLOv5s

YOLOv5s-FSDA: Fire and Smoke Detection Algorithm Based on Im...

引用

International Conference on Digital Home (ICDH)

作者： Yuhua Li Yanru Xu Mengyue Zhang Jianqiang Sheng Software Engineering College Zhengzhou University of Light Industry Zhengzhou China Shenzhen Institute of Information Technology Shenzhen China

ISBN: (数字)9798331509873

ISBN: (纸本)9798331509880

Fire and smoke detection are critical to reducing fire losses. Edge devices with limited resources require a model that is more lightweight while still maintaining the detection accuracy necessary for fire and smoke detection tasks. To solve these problems, this paper proposes a fire and smoke detection algorithm based on YOLOvSs, named YOLOvSsFSDA. Firstly, we incorporate the Cross-scale Feature Fusion (CFF) structure, enhancing the recognition and representation capabilities of features across different scales. Secondly, to further reduce the model burden, we adopt Ghost Convolution (GhostConv) technology to reduce model parameter count and computational load. Thirdly, to enhance the model's overall grasp of fire and smoke areas, we integrate the Content-Aware ReAssembly of Features (CARAFE) upsampling method. In addition, we introduce the Similarity Intersection over Union (SIoU) loss function to reduce the positioning error between the prediction and actual boxes, ensuring that fire and smoke targets of different scales can be located more accurately. Experimental results show that the YOLOvSs-FSDA algorithm, while maintaining the high detection accuracy of YOLOvSs, reduces model complexity by 30% and computational costs by 18%.

关键词： YOLO Accuracy Convolution Computational modeling Image edge detection Prediction algorithms Computational efficiency Complexity theory detection algorithms Load modeling

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Research on Lightweight Fire detection Algorithm Based on YOLOv5s

Research on Lightweight Fire Detection Algorithm Based on YO...

引用

IEEE International Conference of Intelligent Robotic and Control Engineering (IRCE)

作者： Wei Tang He Zhang YuJie Hou AoBo Duan YingWei Liu GuoQing Zhou Qian Li XiaoHu Zheng College of Electrical and Control Engineering Shaanxi University of Science & Technology Xi’an China

ISBN: (数字)9798350352399

ISBN: (纸本)9798350352405

Aiming at the problems of complex structure and heavy calculation of existing fire detection algorithms, a lightweight fire detection algorithm based on YOLOv5s is proposed in this paper. Considering the characteristics of rapid fire spread, the algorithm specifically optimizes the Backbone and Neck structure of YOLOv5s network to achieve rapid and accurate identification of initial flame and smoke. The basic idea of the algorithm is as follows: Firstly, the Conv module and C3 structure in C3darknet53, the backbone network of YOLOv5s, are replaced with GhostConv and C3Ghost respectively, and auxiliary feature maps are introduced to expand the receptor field of the backbone network and improve the detection efficiency; Secondly, Gsconv convolution is used to replace the conventional convolution operation, which can reduce the number of parameters and computation of the neck network while maintaining the semantic information connection of the channel. The experimental results show that the proposed algorithm reduces the number of parameters by 44.4% and the amount of computation by 53.2%, while maintaining the stability of its performance, which makes the algorithm suitable for resource-constrained environment and can detect flame smoke quickly and accurately.

关键词： YOLO Training Convolution Semantics Fires Reliability engineering Neck detection algorithms Thermal stability Robots

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Fast-PCANet For Scene Change detection

Fast-PCANet For Scene Change Detection

引用

IEEE International Conference of Intelligent Robotic and Control Engineering (IRCE)

作者： Siqi Shi Pengcheng Xia Nanting Li Youwei Yin School of Automation and Information Engineering Xi'an University of Technology Xi’an China Chongqing Fudi Lithium Battery Co. Ltd Chongqing China

ISBN: (数字)9798350352399

ISBN: (纸本)9798350352405

An efficient scene change detection algorithm based on Fast-PCANet is proposed. Given infrared and visible differential images, image blocks with candidate changed points can be distinguishes roughly from the unchanged pixel points by the calculated pixel classification threshold. As for Fast-PCANet which preserves detailed information and improves detection efficiency, feature maps of two principal component analysis convolutional layers are merged and concatenated to one dimensional feature vector, then a linear support vector machine is used to predict those changed pixels. The morphological closed operation and median filter are used to further correct false positives. For training Fast-PCANet, the proportion of negative samples is increased by sampling pseudo-changed points with a second-order arithmetic sequence, which can achieve the balance of between positive and negative samples. Experimental results shows the proposed algorithm effectively eliminates missed and false detection and and improves detection performance.

关键词： Training Support vector machines Change detection algorithms Merging Feature extraction Vectors detection algorithms Robots Principal component analysis Arithmetic

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Research on YOLOv8 detection Algorithm Based on SKAttention Fusion

Research on YOLOv8 Detection Algorithm Based on SKAttention ...

引用

Machine Learning and Computer Application (ICMLCA), International Conference on

作者： Linpo Shang Yanzuo Chang Yi Chen Liuyi Yu Zhenbang Zeng Jinping Chen Department of Energy and Power Engineering Guangdong University of Petrochem Technology (GDUPT) Maoming Guangdong China Department of Mechanical and Electronic Engineering Gandong University (GDU) Fuzhou JiangXi China

ISBN: (数字)9798331530334

ISBN: (纸本)9798331530341

This study proposes a YOLOv8 model that integrates SKAttentionn mechanism to improve the recognition ability of flames for object detection algorithms. This article analyzes the challenges and requirements of eliminating safety hazards in the current production environment, and introduces the relevant structure of the YOLOv8 network model and the principle of the SKAttentionn mechanism. The model was validated using a self built standard flame dataset, and comparative experiments with ECA and GAM modules were also added to evaluate the detection performance of the YOLOv8 model with SKAttentionn mechanism. The experimental results show that the detection accuracy of the improved YOLOv8 model is higher than that of the original model and the model with ECA and GAM added, which has certain application value.

关键词： Adaptation models Accuracy Fires Production Object detection Manuals Inspection Personnel detection algorithms Standards

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Vehicle detection Based on Improved YOLOv8

Vehicle Detection Based on Improved YOLOv8

引用

IEEE International Conference on Artificial Intelligence in Engineering and Technology (IICAIET)

作者： Chin Hong Lim Tee Connie Kah Ong Michael Goh Faculty of Information Science & Technology Multimedia University Melaka Melaka Malaysia

ISBN: (数字)9798350389692

ISBN: (纸本)9798350389708

Vehicle detection is a crucial component of Intelligent Transportation System (ITS). With the advancement of deep learning and computer vision, state-of-the-art algorithms such as You Only Look Once version 8 (YOLOv8) have been developed to address the real-time detection challenges. However, during the experimentation process, we observed a class imbalance problem in the self-collected Malaysian traffic dataset. In this paper, we analyse this problem in detail and propose a Simplified Variant of Oversampling (SVO) method to mitigate it. By applying the proposed method, we can ensure that the detection algorithm performs consistently across all classes, thereby improving the overall accuracy and dependability of the system. The measurements used in this paper are mean Average Precision (mAP), precision and recall. The oversampling approach improved mAP50 and mAP50-95 by 16.91 % and 29.43%, respectively. Additionally, different attention mechanisms for instance Triplet Attention, Squeeze and Excitation Networks (SE) and Large Selective Kernel Network (LSKNet), were integrated into the backbone of the detection network to further enhance performance. Among these, the LSKNet attention mechanism achieved the best performance, further improving the mAP50 and mAP50-95 by 2.46% and 2.70%, respectively.

关键词： YOLO Deep learning Attention mechanisms Vehicle detection Lighting Real-time systems Recording Kernel detection algorithms Intelligent transportation systems

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Research on Complex Street View detection Algorithm Based on YOLOv8

Research on Complex Street View Detection Algorithm Based on...

引用

Signal Processing and Intelligent Computing (SPIC), International Conference on

作者： Haiyang Du Jun Liu School of Automation and Electrical Engineering Shenyang Ligong University Shenyang China

ISBN: (数字)9798350368888

ISBN: (纸本)9798350368895

The current street scene is becoming more and more complex, with high pedestrian and vehicular traffic and a large number of targets. Aiming at the problems that the accuracy of the current complex street scene-oriented detection algorithms is not satisfactory and the leakage rate of small targets is unstable, the intelligent detection algorithm YOLOv8s- for complex street scenes is proposed, aiming at the intelligent detection of multi-targets in daily street scenes. This study is based on the target detection model of YOLOv8s, proposing a C2f-DCNv2 module that can sample features flexibly; adding a small target detection layer in the head; and using the Shape IoU loss function. The improved algorithm improves the precision, recall, and average accuracy by 2.2%, 3.6%, and 5.0%, respectively, compared with the initial algorithm, and these experimental data validate that this study can have a good performance when targeting the detection task of complex street scenes.

关键词： Performance evaluation Accuracy Pedestrians Shape Urban areas Signal processing algorithms Object detection Real-time systems Mobile handsets detection algorithms

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An End-to-end Campus Fire detection Algorithm Incorporating Priori Features

An End-to-end Campus Fire Detection Algorithm Incorporating ...

引用

Robotics, Intelligent Control and Artificial Intelligence (RICAI), International Conference on

作者： Yu Liang Xinlin Xie Xuan Liu Jie Wang Shanxi Key Laboratory of Advanced Control and Industrial Intelligence Intelligence School of Electronic Information Engineering Taiyuan University of Science and Technology Taiyuan China

ISBN: (数字)9798331541699

ISBN: (纸本)9798331541705

To address the imbalance in detection speed and accuracy caused by smoke, flame, and background confusion in campus video surveillance scenarios, an end-to-end campus fire detection algorithm incorporating priori features is proposed. First, a feature pyramid fusion module guided by priori features is constructed to cascade the multi-scale feature fusion module. The priori features are individually pre-trained by Convolutional Neural Network (CNN) classification network. The feature complementarity between the two is utilized to improve the feature representation of the detection network. Second, a lightweight set prediction detection head is designed. The head does not require manually designed anchors and post-processing strategies, enabling end-to-end detection while balancing speed and accuracy. Finally, a joint loss function that integrates the losses of categorical and bipartite matching is proposed. Focus on the drawbacks of the slow convergence associated with bipartite matching, both classification information and bipartite bit matching are considered in the detection results. Experimental results on the fire detection dataset demonstrate that the proposed algorithm improved average accuracy by 7.8% while maintaining detection speed, effectively balancing speed and accuracy in fire detection.

关键词： YOLO Training Visualization Accuracy Feature extraction Transformers Video surveillance Convolutional neural networks detection algorithms Testing

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Focused: An Approach to Framework-Oriented Cross-Language Link Specification and detection

Focused: An Approach to Framework-Oriented Cross-Language Li...

引用

International Conference on Software Maintenance (ICSM)

作者： Ailun Yu Yifan Shi Bo Shen Wei Zhang Haiyan Zhao Guangtai Ling Tianyong Wu Zhi Jin Key Laboratory of High Confidence Software Technologies (Peking University) MoE China School of Computer Science Peking University Beijing China Huawei Technologies Co. Ltd. Beijing China

ISBN: (数字)9798350395686

ISBN: (纸本)9798350395693

Framework-based multilingual software development (MLSD) is becoming prevalent in software engineering practice. Despite the advantages, framework-based MLSD also leads to reduced understandability and changeability of multilingual software, due to the introduced cross-language links (XLLs). To help alleviate this problem, there are existing practice and research crafting rules to specify and detect XLLs, but only focusing on specific frameworks. With the intention of coping with the diversity of XLL conventions across different multi-lingual frameworks, this paper proposes Focused, an extensible approach to framework-oriented cross-language link specification and detection. The basic idea is to decouple the two activities of XLL specification and detection as much as possible by mediating between them with a set of DSL-enabled XLL rules, making Focused configurable to different multilingual frameworks. We evaluated Focused on 3 widely-used multilingual frameworks and 15 high-starred open-source projects using these frameworks, showing the expressiveness, effectiveness, and efficiency of Focused.

关键词： Software maintenance Focusing Programming Real-time systems Multilingual DSL detection algorithms Software engineering Software development management

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Measuring Video QoE from Encrypted Traffic: A Collaborative Approach Using Machine Learning and Stalling detection

Measuring Video QoE from Encrypted Traffic: A Collaborative ...

引用

International Conference on Network Protocols

作者： Longjie Wang Wenyong Wang Wei Jiang Bin Zhang Qixun Zhu Jun Deng Junrui Wu School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu China China Telecom Sichuan Branch Chengdu China Sichuan Communication Surveying & Design Institute Co. Ltd Chengdu China NSFOCUS Information Chengdu Technology Co. LTD Chengdu China

ISBN: (数字)9798350351712

ISBN: (纸本)9798350351729

Video stream has become the largest part of the Internet traffic, and video Quality of Experience (QoE) is an important factor affecting the overall QoE of Internet users. Accurate measurement of video $\mathbf{Q o E}$ from the perspective of traffic can help network operators to better plan network resources and improve user retention. To accurately measure video QoE through traffic, multiple factors need to be considered, with stalling being a significant one. Mean Opinion Score (MOS) is an indicator that quantifies video QoE. Traditionally, labeling MOS for videos requires subjective quality assessment experiments, which is time-consuming and labor-intensive. Second, the statistical features used by traditional machine learning methods are difficult to represent the stalling events in video steams, leading to inaccurate MOS measurements. Therefore, a number of past studies only estimate the indirect metrics that affect MOS, such as resolution and bitrate. In this paper, we use the parametric model ITU-T P. 1203 to automatically label MOS for videos. And we propose a two-stage approach to accurately measure MOS from video traffic. First, we introduce a stalling detection algorithm to identify stalling events from HTTP Adaptive Streaming (HAS) traffic. Then, we use these stalling events to produce secondary features and combine them with other statistical video stream features to establish a machine learning model for direct MOS measuring. Experiments show that our proposed method reduces the Root Mean Squared Error (RMSE) of MOS estimation from 0.455 to 0.406 compared to the conventional method.

关键词： Estimation Machine learning Streaming media Feature extraction Quality assessment Internet Quality of experience detection algorithms Usability Video recording

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Network Intrusion detection in Cloud Computing Environments

Network Intrusion Detection in Cloud Computing Environments

引用

Applied Intelligence and Computing (AIC), IEEE World Conference on

作者： Madhusudhan Dasari Sreeramulu K. Suganyadevi Anuteja Reddy Neravetla Abdul Sajid Mohammed Dept of Cyber Security American Express USA Department of ECE Sri Eshwar College of Engineering Coimbatore Tamil Nadu India Department of Information Technology University of The Cumberlands Kentucky USA Dept of School of Computer and Information Sciences University of the Cumberlands Kentucky USA

ISBN: (数字)9798350384598

ISBN: (纸本)9798350384604

The increasing use of cloud computing has also resulted in a higher vulnerability to cyber attacks. Network Intrusion detection Systems (NIDS) play a vital role in protecting cloud computing environments from these attacks. However, the unique characteristics of cloud computing, such as its dynamic and distributed nature, pose challenges for traditional NIDS. This paper provides an overview of the current state of NIDS in cloud computing environments, including their limitations and potential solutions. It also discusses the use of machine learning and artificial intelligence techniques in improving NIDS performance. The effectiveness of NIDS in detecting both known and unknown attacks is crucial for the security of cloud computing environments and requires continuous research and development.

关键词： Cloud computing Network intrusion detection Organizations Machine learning Network security Data breach detection algorithms Cyberattack Research and development Monitoring

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