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检索条件"主题词=detection algorithms"

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Grasp Manipulation Relationship detection based on Graph Sample and Aggregation

Grasp Manipulation Relationship Detection based on Graph Sam...

引用

IEEE International Conference on Robotics and Automation (ICRA)

作者： Jiayuan Luo Yaxin Liu Han Wang Mengyuan Ding Xuguang Lan National Key Laboratory of Human-Machine Hybrid Augmented Intelligence National Engineering Research Center of Visual Information and Applications Institute of Artificial Intelligence and Robotics Xi’an Jiaotong University Xi’an China

ISBN: (数字)9798350384574

ISBN: (纸本)9798350384581

In multi-object stacking scenarios, exploring the relationships among objects and determining the correct sequence of operations are crucial for robotic manipulation. However, previous algorithms inefficiently combine global and local information, often focusing solely on the local features of objects or the interactions of object features at a global level. This approach leads to imbalanced distribution of features and the generation of redundant or missing relationships in complex scenes, such as multi-object stacking and partial occlusion. To address this issue, we have developed a grasp manipulation relationship detection algorithm called Graph Sampling Aggregation Network for Visual Manipulation Relationship detection (GSAGED). This algorithm assists robots in detecting targets in complex scenes and determining the appropriate grasping order. Firstly, the Positional Encoding Module in GSAGED enhances object feature information by considering global contexts. Secondly, the Graph Sampling Aggregation method effectively integrates global and local information, relieving imbalanced distribution of features. Finally, we applied the developed algorithm to a physical robot for grasping. Experimental results on the Visual Manipulation Relationship Dataset (VMRD) and the large-scale relational grasp dataset named REGRAD demonstrate that our method significantly improves the accuracy of relationship detection in complex scenes and exhibits robust generalization capabilities in real-world applications.

关键词： Visualization Accuracy Stacking Focusing Grasping Encoding detection algorithms

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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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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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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 ...

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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

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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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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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Research on Complex Street View detection Algorithm Based on YOLOv8

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

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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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UAV-based infrard thermography for the fine highvoltage insulators detection

UAV-based infrard thermography for the fine highvoltage insu...

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

作者： Lihong Xu Xinyu Liu State Grid Fujian Electric Power Company Putian China School of Electrical Engineering and Automation Fuzhou University Fuzhou China

ISBN: (数字)9798331506612

ISBN: (纸本)9798331506629

Toward the fine detection of high-voltage insulators, this paper proposes a framework for fine detection of high-voltage insulators based on infrared thermal imaging data. The framework realizes high-precision detection of insulators and fine detection of the number of insulator tiles through the improved YOLOv5s detection algorithm combined with the designed density clustering peak detector. The improved YOLOv5s detection algorithm embedded with the designed CDA attention mechanism enhances the global sensing capability. Meanwhile, the density clustering peak detector designed based on density clustering combined with moving smooth filtering realizes the fine counting of insulator tiles in infrared thermal imaging. In order to verify the effectiveness of the proposed framework, an infrared thermal imaging insulator detection dataset from the UAV perspective is built and compared with typical detection models and benchmark models. The experimental results show that the proposed method achieves to in detection accuracy to realizes the dual tasks of accurate insulator detection and fine counting of insulator tile number, which provides a new solution for the fine detection of insulators in high altitude power transmission lines.

关键词： YOLO Accuracy Power transmission lines Thermal engineering Imaging High-voltage techniques Detectors Insulators detection algorithms Software engineering

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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...

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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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