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检索条件"主题词=detection algorithms"

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An improved YOLOv8-based safety helmet wearing detection algorithm

An improved YOLOv8-based safety helmet wearing detection alg...

引用

Computer Information Science and Application Technology (CISAT), International Conference on

作者： Shiyong Ning Xu Han Computer and Information Engineering Department Harbin University of Commerce Harbin China

ISBN: (数字)9798350375107

ISBN: (纸本)9798350375114

Whether to wear a safety helmet is one of the key measures to ensure the safety of personnel in the construction process. This paper proposes a new detection algorithm for safety helmet wearing. Utilizing the YOLOv8n architecture, the algorithm incorporates a SENetV2-based channel-wise attention mechanism into the network’s backbone, thereby giving full consideration to global information, thereby allowing the network to allocate more attention to the safety helmet. To overcome the deficiencies of the initial backbone in combining features, we substitute the original network’s neck section with the CCFM module, thereby improving YOLOv8n’s capacity for feature integration following the extraction of features from the backbone network. A new detection head, DyHead3, is proposed to replace the detection head in the original network model. The empirical findings indicate that the algorithm enhances the mAP, P, and R metrics by 2.02%,3.41%,and 1.07%, respectively, when contrasted with the baseline YOLOv8n approach, respectively, which fully demonstrates the effectiveness and practicality of the improved algorithm proposed in this paper.

关键词： Measurement Information science Head Computational modeling Buildings Feature extraction Safety Neck Personnel detection algorithms

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Road Trash detection Algorithm of RT-DETR Integrated with State Space

Road Trash Detection Algorithm of RT-DETR Integrated with St...

引用

Image Processing, Computer Vision and Machine Learning (ICICML), International Conference on

作者： Zexing Luo Guoqing Yang Xinyong Zhao Yeqin Shao School of Transportation and Civil Engineering Nantong University Nantong China Suzhou Research Institute of Industrial Technology Zhejiang University Hangzhou China Hualuyiyun Technology Co. Ltd NanJing China

ISBN: (数字)9798350355413

ISBN: (纸本)9798350355420

As global urbanization progresses rapidly, the issue of urban road trash is becoming increasingly severe. Traditional manual cleaning methods are inefficient and struggle to meet the growing demand. This paper proposes an improved road trash detection algorithm based on RT-DETR (Real-Time detection Transformer) and State Space Model (SSM) to enhance detection accuracy and robustness. The algorithm incorporates an innovative feature pyramid structure and a state-space-based internal scale feature enhancement module, effectively addressing object detection in complex scenes as well as challenges associated with occlusions and small object detection. We constructed a diverse urban road trash detection dataset comprising over 60,000 images, providing a solid foundation for the training and testing of the algorithm. Experimental results show that the proposed model improves the mean average precision (mAP) on the COCO dataset by 0.8 percentage points and achieves a 1.2 percentage point improvement on our custom road trash detection dataset, with a 2.1 percentage point increase in the precision of small object detection. The proposed algorithm achieves a favorable balance between real-time performance and accuracy, significantly enhancing trash detection in complex environments, particularly in terms of small object recognition and detection accuracy under occlusion conditions.

关键词： Accuracy Roads Computational modeling Urban areas Object detection Feature extraction Robustness Real-time systems Object recognition detection algorithms

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On-Road Water detection and Water Film Height Estimation using a Time-of-Flight Camera

On-Road Water Detection and Water Film Height Estimation usi...

引用

International Conference on Intelligent Transportation

作者： Dzenana Puscul Armin Schönlieb Andreas Ramsauer Cornelia Lex Institute of Automotive Engineering Graz University of Technology Graz Austria Development Center Graz Infineon Technologies AG Austria Department for Powertrain Engineering (PTE) AVL List GmbH Austria

ISBN: (数字)9798331505929

ISBN: (纸本)9798331505936

In this article, a methodology to detect the presence of water on the road with a time-of-flight (ToF) camera is presented and validated with measurements from a proving ground. The water detection algorithm distinguishes between dry and wet roads and is based on the measurement data of two time-of-flight imagers. Furthermore, the water film height is estimated with a feedforward neural network. The presented measurements were carried out on the stationary vehicle. The variations of measured conditions include different lighting conditions (daylight, artificial light at night, dawn period) as well as dry and wet asphalt with different water film levels. The results show that the water detection is reliable and the water film height can be well estimated by the neural network.

关键词： Asphalt Accuracy Roads Estimation Cameras Artificial light Feedforward neural networks Reliability detection algorithms Intelligent transportation systems

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High-precision 3D Facial Landmark detection with Curvature-fused Graph Attention Network

High-precision 3D Facial Landmark Detection with Curvature-f...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Juncheng Han Yuping Ye Di Wu Shiyang Long Zhan Song Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen China University of Chinese Academy of Sciences Beijing China School of Smart Marine Science and Engineering Fujian University of Technology Fuzhou China

ISBN: (数字)9798350386226

ISBN: (纸本)9798350386233

With the rapid advancement of deep learning, 2D facial landmark detection algorithms have achieved satisfying results. However, due to the absence of large-scale accurately annotated 3D facial datasets, most current 3D facial landmark detection algorithms rely on 2D texture assistance or non-real digital 3D faces. The performance of these algorithms is limited by the accuracy of 2D texture mapping onto 3D faces and the difference between digital faces and actual faces. To tackle these challenges, we have built a large-scale, high-precision 3D facial database using a structured light system. Facial landmarks within the database are marked multiple times to ensure accuracy. Building upon this foundation, we proposed a novel point cloud sampling method and 3D facial landmark detection algorithm. This method utilizes a curvature-fused graph attention network (CGAT) to directly predict landmark coordinates from 3D point clouds. Initially, we extracted a subsampled point set carrying curvature information from the original 3D facial point cloud via geometric point sampling (GPS). Then, we used curvature-encoded positional information as the learning component of the attention module and employed it as a feature extractor to construct the CGAT. We assessed the performance of CGAT on two datasets, BU-3DFE and CIE-H3DF (Ours). Compared to the existing facial landmark detection algorithms, CGAT achieves higher accuracy.

关键词： Point cloud compression Three-dimensional displays Accuracy Databases Feature extraction Sampling methods Data mining detection algorithms Faces Global Positioning System

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Hybrid GMM-GS Message Passing detection for OTFS Modulation

Hybrid GMM-GS Message Passing Detection for OTFS Modulation

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IEEE Conference on Wireless Communications and Networking

作者： Wenfang Fan Yuehua Ding Yide Wang School of Electronic and Information Engineering South China University of Technology Guangzhou China Institut d'Electronique et Des Technologies du NumeRique CNRS UMR6164 Nantes Université Nantes France

ISBN: (数字)9798350303582

ISBN: (纸本)9798350303599

Orthogonal Time Frequency Space (OTFS) modulation can effectively combat the Doppler effects and exploit the time-frequency resources. The sparsity of the OTFS channel matrix in delay-Doppler domain plays a crucial role in message passing detection algorithms. This paper presents a hybrid detection approach that combines Gaussian mixture model (GMM) with Gaussian approximation (GS). According to the path gain, GMM is employed to deal with significant paths and GS for less significant paths. The proposed hybrid method achieves a balance between computational complexity and performance by indirectly computing messages and introducing a damping factor. Simulation results show that the proposed method exhibits noteworthy Bit Error Rate (BER) performance and reduces the computational complexity associated with GMM.

关键词： Damping Time-frequency analysis Message passing Simulation Bit error rate Modulation detection algorithms

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SeLog: A Log Anomaly detection Method on Log Event and Variable Semantic

SeLog: A Log Anomaly Detection Method on Log Event and Varia...

引用

International Conference on Information Systems and Computer Aided Education (ICISCAE)

作者： Cheng Li Guang Chen Xiaoxi Mi Yucheng Zhang Hongwei Zhou Information Engineering University Zhengzhou China

ISBN: (数字)9798350350760

ISBN: (纸本)9798350350777

Detecting log anomalies based on semantics is an important approach. However, the existing methods often ignore the semantic information of variables. Variables in logs contain important indicative information, which is of positive significance for anomaly detection. Therefore, this paper proposes a log anomaly detection method based on events and fusion variable semantics, referred to as SeLog. SeLog is based on the method of keyword table and positive antonym table, combined with expert experience, to extract valuable variable information from the log. We use these valuable log variables and events as data sources, use the FastText algorithm to obtain their semantics, and construct an input vector suitable for the Bi-LSTM model to detect log anomalies by learning the semantic information of the log context. Experimental verification shows that the F1 value of the model reaches 99 %, which is better than baseline methods such as DeepLog.

关键词： Training Soft sensors Semantics Vectors Data models detection algorithms Anomaly detection Testing Information systems Context modeling

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Improved YOLOv8s-Based Spray Code detection Algorithm

Improved YOLOv8s-Based Spray Code Detection Algorithm

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Electronic Information Engineering and Computer Science (EIECS), 2021 International Conference on

作者： Yin Wang Likun Lu Yali Qi Qingtao Zeng School of Information Engineering Beijing Institute of Graphic Communication Beijing China

ISBN: (数字)9798331531409

ISBN: (纸本)9798331531416

The traditional method of code detection relies on manually extracting barcodes and QR codes from paper, which is a cumbersome process and prone to human error, leading to low detection efficiency and insufficient accuracy. To address these issues, this paper proposes an improved YOLOv8s-based code detection algorithm that incorporates the LSK attention mechanism and the WIoU loss function, enabling automatic recognition and localization of barcodes and QR codes on printing devices. Compared to traditional methods, this algorithm significantly improves detection efficiency and accuracy, reduces manual intervention, and optimizes the debugging process. Experimental results show that the detection accuracy of the algorithm exceeds 0.81 and is capable of maintaining efficient and accurate detection performance in complex backgrounds and diverse printing environments.

关键词： Printing Performance evaluation Accuracy Codes Attention mechanisms QR codes Production Printers detection algorithms Testing

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Lightweight Tunnel Leakage Water detection Algorithm Based on YOLOv8n

Lightweight Tunnel Leakage Water Detection Algorithm Based o...

引用

System Simulation Technology and its Application (CCSSTA), IEEE China Conference on

作者： Shiwen Wang Jiaqing Mo Liang Xu Xinqi Zheng School of Computer Science and Technology Xinjiang University Urumqi China School of Electrical Engineering and Automation Tianjin University of Technology Tianjin China

ISBN: (数字)9798350366600

ISBN: (纸本)9798350366617

Tunnel leakage is one of the biggest hazards to shield tunnels, this paper proposes a lightweight tunnel water leakage target detection algorithm that improves YOLOv8n for the shortcomings of the existing tunnel water leakage target detection algorithms, such as poor precision and too many model parameters. To make feature fusion richer and reduce the model parameters, this paper proposes to use a bi-directional feature pyramid network on the head network. In addition, this paper uses multi-scale convolutional attention to adapt to the complex features of the leakage area in the tunnel. Finally, use the dilated reparam block to reduce the computational costs and expand the receptive field. The algorithm in this paper not only improves the detection accuracy compared to the original algorithm, but also significantly reduces the model parameters and computational costs. In addition, the superiority of this paper’s algorithm is verified through ablation experiments and comparisons with other mainstream target detection algorithms.

关键词： Adaptation models Accuracy Computational modeling Object detection Bidirectional control Hazards Computational efficiency Systems simulation detection algorithms

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An Efficient Fire detection Algorithm with Dataset Generation Based on Diffusion

An Efficient Fire Detection Algorithm with Dataset Generatio...

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Chinese Automation Congress (CAC)

作者： Gaoyang Wang Hongtao Zheng Duo Xiao College of Control science and Engineering Zhejiang University Hangzhou China School of Information & Electrical Engineering Hangzhou City University Hangzhou China The academy of Edge Intelligence Hangzhou City University Hangzhou China

ISBN: (数字)9798350368604

ISBN: (纸本)9798350368611

Faced with increasing urbanization and population growth, the hazard of fires has become a critical issue in urban areas worldwide. Therefore, the focus on how to achieve accurate and rapid fire detection using artificial intelligence has intensified. Currently, fire detection algorithms often struggle to balance precision and processing speed. At the same time, high-quality fire detection datasets are extremely scarce, severely limiting the performance of algorithms. To address these challenges, this paper first proposes an enhanced fire detection algorithm framework based on YOLOv8, aiming to comprehensively improve the accuracy and processing speed of detection. This framework integrates Swin Transformer and Slim Neck to enhance model performance, and utilizes WIoUv3 for network training regularization. Additionally, this paper introduces a novel fire detection dataset generation algorithm: Fire-Generator, to address the scarcity of high-quality training data. This tool utilizes a latent diffusion model to guide image generation, enabling the synthesis of training data from textual descriptions or existing images. Experimental evaluations demonstrate that our method outperforms most currently available fire detection algorithms in terms of performance. The versatility of Fire-Generator extends beyond fire detection, demonstrating potential for generating training data across different domains. Through this comprehensive strategy, our model achieves significant results by leveraging both real and synthesized data.

关键词： Training Accuracy Limiting Image synthesis Urban areas Training data Fires Transformers Neck detection algorithms

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HSV histogram-based method for fire detection in smart cities

HSV histogram-based method for fire detection in smart citie...

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8th IET Smart Cities Symposium (SCS 2024)

作者： N. N. Apaydin K. Dinc O. Yaman M. Karakose Digital Forensic Engineering Fırat Üniversitesi Elazığ Turkey Computer Engineering Fırat Üniversitesi Elazığ Turkey

ISBN: (数字)9781837243105

Global warming and fires are the big problems we face today. Fires, in particular, have caused loss of life and property worldwide in recent years. Early detection of fires in cities and forest areas is very important. In this study, machine learning methods were utilized for the early detection of fires. Histogram values obtained using the HSV color space were processed for the purpose of fire detection. Separate results were obtained using the Zenmuse and Phantom cameras from the FLAME dataset. It was observed that the HSV color space provided better results than the RGB and grayscale representations. For the Zenmuse Camera, the best result was achieved with the Fine KNN model, with an accuracy rate of 99.90%. For the Phantom Camera, the best result was achieved with the Cubic SVM model, with an accuracy rate of 99.64%.

关键词： Climate change Global warming Fires detection algorithms Smart cities Histograms Image color analysis Accuracy Cameras Urban areas Machine learning Gray-scale Nearest neighbor methods

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