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检索条件"主题词=detection algorithms"

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Cross-Layer Cascading Chain-of-Thought Based Object detection Algorithm for Aerial Images in Foggy Weather

Cross-Layer Cascading Chain-of-Thought Based Object Detectio...

引用

International Conference on Big Data and Information Analytics (BigDIA)

作者： Xuanyu Zhang Jian Huang Sihang Zhou Dexing Wei Changyu Qian Zexin Li College of Intelligence Science and Technology National University of Defense Technology Changsha China

ISBN: (数字)9798350354621

ISBN: (纸本)9798350354638

In response to the challenges posed by fog obstruction and small object in the detection of aerial images under foggy conditions, this paper presents an image restoration network (CLC-CPA) based on the cross-layer cascading chain-of-thought prompt mechanism that can be integrated with detection algorithms. The proposed network executes the restoration strategy step by step, with each step based on the results of the previous step (i.e., chain-of-thought mode), gradually restoring foggy images to a level conducive to detection. Additionally, it combines deep and shallow information via cross-layer cascading to preserve the features of small objects. Furthermore, the HorNet is employed to enhance the backbone network of the detection algorithm, extending feature extraction into higher-order spaces, which enables the network to capture more distinguishable spatial relationships and improves the detection accuracy of small objects in aerial images. Finally, utilizing the VisDrone dataset, a foggy scene aerial image dataset (FoggyVisDrone) is constructed based on the atmospheric scattering model. Experimental results indicate that, in the test set of aerial images with varying fog concentrations, the mAP50 of the improved algorithm exceeds that of the baseline algorithm by more than 4.4%.

关键词： Cross layer design Accuracy Atmospheric modeling Scattering Object detection Big Data Feature extraction Image restoration detection algorithms Meteorology

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Opportunistic Network Selfish Node detection Algorithm Based on Credibility Combining Energy and Cache

Opportunistic Network Selfish Node Detection Algorithm Based...

引用

International Conference on Computer Supported Cooperative Work in Design

作者： Kun Yan Hao Hu Gang Xu Lijie Li Yanhe Fu Gaofeng Zhang College of Computer Science Inner Mongolia University Hohhot China

ISBN: (数字)9798350349184

ISBN: (纸本)9798350349191

The existing selfish node detection algorithms for opportunistic networks do not consider the impact of node energy and cache on node selfishness, resulting in inaccuracies and high latency in the detection algorithm. Therefore, this paper proposes a selfish node detection algorithm based on credibility combined with energy and cache (CDEC). Firstly, a new credibility model is presented. Then, through an analysis of the residual energy and cache usage ratio of nodes over time, selfish nodes have greater differences compared to cooperative nodes. Finally, the overall service quality of nodes is combined with their credibility, residual energy, and cache usage ratio to detect selfish nodes based on their service quality. Simulation results show that compared with existing Watchdog and 2-ACK detection algorithms, the CDEC algorithm can effectively improve the accuracy of selfish node detection and message delivery rates while reducing network latency.

关键词： Accuracy Federated learning Simulation detection algorithms

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Robust Multimodal Cough and Speech detection using Wearables: A Preliminary Analysis

Robust Multimodal Cough and Speech Detection using Wearables...

引用

Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

作者： Yuhan Chen Jeffrey Barahona Iype Eldho Yanbing Yu Riyaz Muhammad Bill Kutsche Michelle L. Hernandez Delesha Carpenter Alper Bozkurt Edgar Lobaton Department of Electrical and Computer Engineering North Carolina State University Raleigh NC USA Murata Electronics North America Inc. Smyrna GA USA Department of Pediatrics School of Medicine University of North Carolina at Chapel Hill Chapel Hill NC USA Division of Pharmaceutical Outcomes and Policy Eshelman School of Pharmacy University of North Carolina at Chapel Hill Chapel Hill NC USA

ISBN: (数字)9798350371499

ISBN: (纸本)9798350371505

Cough detection is a crucial tool for long-term monitoring of respiratory illnesses. While clinical methods are accurate, they are not available in a home-based setting. In contrast, wearable devices offer a more accessible alternative, but face challenges in ensuring user speech privacy and detecting coughs accurately in real-world settings due to potential poor audio quality and background noise. This study addresses these challenges by developing a small-size multimodal cough and speech detection system, enhanced with an Out-of-Distribution (OOD) detection algorithm. Through our analyses, we demonstrate that combining transfer learning, a multimodal approach, and OOD detection techniques significantly improves system performance. Without OOD inputs, the system shows high accuracies of 92.59% in the in-subject setting and 90.79% in the cross-subject setting. With OOD inputs, it still maintains overall accuracies of 91.97% and 90.31% in these respective settings by incorporating OOD detection, despite the number of OOD inputs being twice that of In-Distribution (ID) inputs. This research are promising towards a more efficient, user-friendly cough and speech detection method suitable for wearable devices.

关键词： Voice activity detection Privacy Accuracy System performance Transfer learning Wearable devices Engineering in medicine and biology detection algorithms Monitoring Faces

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SM-YOLOv8 detection algorithm for traffic signs

SM-YOLOv8 detection algorithm for traffic signs

引用

Computer Technology and Information Science (ISCTIS), International Symposium on

作者： Junlong Zhang Wenhua Han College of Automation Engineering Shanghai University of Electric Power Shanghai China

ISBN: (数字)9798350354560

ISBN: (纸本)9798350354577

To address the issues of missed detection and false detection in traffic sign detection during daily driving, an improved SM-YOLOv8 algorithm is proposed. In the backbone, Max-Pooling down sampling is used to preserve important feature information and reduce feature loss. The MSCA convolutional attention is embedded to aggregate local and global information for selecting and extracting target features at various scales. The multi-scale cross-layer feature fusion structure SM-Neck is designed to enhance the fusion of semantic information at different levels and effectively capture target features at various scales. The detection performance is further improved by using the Inner method to enhance CIoU. On the TT100K dataset, the accuracy, recall rate, and mAP value are improved by 11.6%, 9.3%, 9.8%, and 7.4% respectively, with a 30% reduction in parameters. Experimental results demonstrate that the proposed method exhibits advantages of high accuracy and low parameter count compared to the original model and other algorithms.

关键词： Cross layer design Information science Accuracy Attention mechanisms Computational modeling Aggregates Semantics Feature extraction Data mining detection algorithms

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Image edge detection based on improved ant colony algorithm

Image edge detection based on improved ant colony algorithm

引用

Intelligent Control, Measurement and Signal Processing and Intelligent Oil Field (ICMSP), International Conference on

作者： Yuehao Yan Juan Hui Gaohui Kong Shuaishuai Wang Liyi Feng Yujie Zhao State Grid Henan Electric Power Company Xingyang Power Supply Company Xingyang China Zhengzhou Xinda Advanced Technology Research Institute Zhengzhou China

ISBN: (数字)9798331516550

ISBN: (纸本)9798331516567

Image edge detection is a necessary work in object recognition, feature extraction and structure analysis. Firstly, an improved ant colony algorithm is proposed to solve the problems of partial path selection, low accuracy and low efficiency of traditional ant colony algorithm. The initial pheromone, heuristic function, pheromone update rule and state transition parameters of ant colony algorithm were improved. Finally, the experimental simulation verifies that two classical images are selected to carry out adaptive edge extraction, and the edge detection effect is compared with the traditional detection algorithm. Experimental results show that the improved algorithm can achieve complete and continuous image edge extraction, and verify the effectiveness of the algorithm.

关键词： Accuracy Image edge detection Heuristic algorithms Noise Signal processing algorithms Clustering algorithms Feature extraction Object recognition detection algorithms Intelligent control

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Lightweight fire detection algorithm based on LSCD-FasterC2f-YOLOv8

Lightweight fire detection algorithm based on LSCD-FasterC2f...

引用

Big Data & Artificial Intelligence & Software Engineering (ICBASE), International Conference on

作者： Bo Yin Department of Mechanical and Electronic Engineering GuiZhou Communications Polytechnic University Gui Yang China

ISBN: (数字)9798331506612

ISBN: (纸本)9798331506629

In this paper, a lightweight fire detection algorithm based on LSCD-FasterC2f-YOLOv8 is proposed to optimize the real-time and accuracy of traditional target detection models in fire scenarios to meet the requirements of efficient monitoring in complex environments. Addressing YOLOv8’s deployment hurdles on limited devices, it innovates in three ways: 1) Introducing FasterC2f, an efficient C2f alternative, cuts parameters and complexity via dimensional reduction. 2) A lightweight LSCD detection head boosts efficiency and accuracy, leveraging shared convolutions to reduce parameters and enhance feature fusion, ensuring precision in challenging fires. 3) Rigorously comparing multiple IoU loss functions optimizes bounding box regression, enhancing detection without extra computation. The experimental results show that the proposed LSCD-FasterC2f-YOLOv8 algorithm achieves excellent performance in the fire detection task, not only achieving significant lightweight in model volume and computational quantity, but also maintaining even higher detection accuracy comparable to the original YOLOv8.

关键词： Solid modeling Accuracy Computational modeling Software algorithms Object detection Feature extraction Real-time systems detection algorithms Monitoring Software engineering

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YOLOv8 Complex Scene Helmet detection Based on Lightweight Improvement

YOLOv8 Complex Scene Helmet Detection Based on Lightweight I...

引用

Computer Technology and Information Science (ISCTIS), International Symposium on

作者： Junqing Bai Weinan Chen Chenxu Zhang Yunjie Zhou Xi'an Shiyou University Xi'an China Fuzhou University Xiamen China Naval Submarine Academy Qingdao China

ISBN: (数字)9798350354560

ISBN: (纸本)9798350354577

Wearing helmets has been a mandatory guideline for construction sites, mining, drilling and other fields. For different complex scenes, there is a wide demand for efficient and accurate helmet detection, and the current helmet detection algorithms have problems such as large number of parameters and weak timeliness. In this regard, a lightweight Faster-GAMv8 helmet detection algorithm is proposed to improve YOLOv8. By introducing the GAM attention mechanism, the correlation between different channels is effectively captured. The method of using FasterBlock module to replace C2f significantly reduces the model parameters and FLOPs operations. Finally, the inclusion of the Wise-IoU loss function enables a more accurate evaluation of the detection results of small targets. Experiments using the VOCdevkit dataset show that the proposed algorithm achieves a mAP of 91.1% and the floating-point computation is reduced to 7.6MB, which is able to better satisfy the demand for fast detection at industrial sites and provides an effective solution for real-time monitoring in the field of industrial safety.

关键词： Head Accuracy Attention mechanisms Convolution Computational modeling Redundancy Feature extraction Robustness Safety detection algorithms

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Dynamic Environmental Target detection and Ranging Based on Improved Yolov5s

Dynamic Environmental Target Detection and Ranging Based on ...

引用

Information Technology,Big Data and Artificial Intelligence (ICIBA), International Conference on IEEE International Conference on

作者： Zeng Wang Meng Li Chunyan Han School of Electrical Engineering University of Jinan China

ISBN: (数字)9798350363609

ISBN: (纸本)9798350363616

In order to improve the detection effect of vehicles in dynamic environment and further obtain accurate distance information, this paper proposes an improved target vehicle detection algorithm with YOLOv5s and ranging of the vehicle in front with binocular depth camera. Based on the YOLOv5s detection network, firstly, the CA attention module is introduced into the Backbone network to effectively extract the contour features of the detection target, secondly, the PANet network in Neck is replaced with BiFPN to improve the fusion ability of the features, and then the Head detection layer is replaced with the decoupled head method to accelerate the convergence speed of the network, and then the loss function is optimized using the SIoU to enhance the accuracy of the vehicle detection accuracy. The improved YOLOv5s network model is used to constrain the feature matching points to obtain the optimal parallax value, and finally the distance information of the front vehicle is obtained through the principle of binocular vision ranging. Tests show that the vehicle recognition rate accuracy is 79.50% with an improvement of 1.50% and the average error rate of ranging is 2.552% in a distance range of 14m.

关键词： YOLO Accuracy Error analysis Heuristic algorithms Feature extraction Distance measurement Trajectory Neck Vehicle dynamics detection algorithms

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Vision Based Robotic-Grasping Pose Estimation Using Object Corner detection and Depth Images of Densely Packed Scenes

Vision Based Robotic-Grasping Pose Estimation Using Object C...

引用

International Conference on Emerging Technologies and Factory Automation (ETFA)

作者： Husnu Melih Erdogan Marek Perkowski Portland State University Siemens Corporation Portland State University

ISBN: (数字)9798350361230

ISBN: (纸本)9798350361247

This study is concerned with the task of robotic piece picking, which has been a persistent challenge in the realm of robotic manipulation. Despite notable progress in this fields, this domain still presents numerous hurdles. This paper presents a robotic grasping detection algorithm that finds sufficiently stable grasping poses for a densely stocked cuboid-shaped packages using the depth image of the picking scene. Using depth sensing and computer vision algorithms, the proposed method computes a highly accurate grasping region and pose for the object of interest. The method achieves 95.17 % success rate on the DPB (Densely Packed Boxes) dataset. The proposed algorithm improves the success rate of the grasping synthesis methods that optimizes the distance to the geometric centroid of an object surface as a stable grasping point by 37.68%.

关键词： Pose estimation Grasping Robot sensing systems Reliability engineering Sensors Object recognition detection algorithms Manufacturing automation Corner detection Noise level

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Early detection of Wildfire using IoT Sensors without Labelled Fire Data

Early Detection of Wildfire using IoT Sensors without Labell...

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International Conference on Sensing Technology, ICST

作者： Qingyang Zhuang Chiu Chun Chan Xiangyun Zhou Salman Durrani School of Engineering Australian National University Canberra ACT Australia

ISBN: (数字)9798350374827

ISBN: (纸本)9798350374834

Wildfires, commonly referred to as bushfires in Australia and forest fires in Europe, pose significant threats to life, property, and the environment. Prompt identification of wildfires is crucial to prevent them from escalating into catastrophic events. This paper considers the use of wireless sensors to measure environmental conditions, in particular carbon dioxide, to detect wildfire at its early stages. We propose a detection algorithm that leverages exclusively non-fire measurement data, without relying on labelled fire data. This differs significantly from earlier research in this field, which generally presumes the existence of labelled fire data. In addition, our algorithm takes into account the temporal and spatial correlations in the sensor measurements to determine the existence of fire. Experimental burns are conducted in a campsite by igniting a small fire from a 0.7 square metres fire pit. The result shows that our algorithm applied to the experimental data can be tuned to minimise false alarms, at the same time detecting small fires within 50 metres and within 4 to 14 minutes after ignition.

关键词： Wireless communication Wireless sensor networks Wildfires Correlation Scalability Ignition Forestry Sensors detection algorithms Testing

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