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检索条件"主题词=detection algorithms"

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Improved YOLOv8n Helmet detection Algorithm for Missed and False detection

Improved YOLOv8n Helmet Detection Algorithm for Missed and F...

引用

Cognitive Computing and Complex Data (ICCD), International Conference on the

作者： Jing Li Chengjun Qiu Yuangan Wang Song Wang College of Mechanical and Marine Engineering Beibu Gulf University Qinzhou China Faculty of Electronics and Information Technology Beibu Gulf University Qinzhou China

ISBN: (数字)9798350352894

ISBN: (纸本)9798350352900

Aiming at the missed and false detection of helmet-wearing detection caused by a small target size or target being occluded in complex working scenarios. An improved helmet-wearing detection algorithm based on YOLOv8n is proposed. First, utilizing the BiFPN enhances the performance in detecting small targets and enhances the capability for generalization and feature fusion. Second, the incorporation of the CA attention mechanism into the backbone network aims to minimize the chances of misclassification, and enhance the precision of detection, thus optimizing the model performance. Finally, Introducing the SioU loss function improves the robustness of the detection. It is concluded from the experiments that the average accuracy of helmet-wearing detection improved from 93.0% to 94.9%, and the average accuracy of non-helmet-wearing detection improved from 91.3% to 92.4%, which is 1.9% and 1.1% respectively. The results show that the improved YOLOv8n algorithm can effectively improve the detection accuracy when the target size is small or the occlusion is missing.

关键词： Head Accuracy Attention mechanisms Feature extraction Robustness Safety detection algorithms Cognitive systems

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Enhancing 3D detection Accuracy in Autonomous Driving through Pseudo-LiDAR Augmentation and Downsampling

Enhancing 3D Detection Accuracy in Autonomous Driving throug...

引用

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

作者： Xi Gong Xin Huang Shanshan Chen Bo Zhang College of Software Nankai University Tianjin China College of Computer and Information Engineering Tianjin Normal University Tianjin China

ISBN: (数字)9798350355413

ISBN: (纸本)9798350355420

3D object detection is crucial for autonomous driving. Currently, most approaches depend on costly LiDAR sensors for acquiring precise depth information. Although pseudo-LiDAR techniques based on stereo cameras offer a more cost-effective alternative, their performance is still inferior to LiDAR-based methods. The objective of this paper is to improve detection accuracy using pseudo-LiDAR augmentation combined with downsampling. Specifically, in terms of pseudo-LiDAR augmentation, the GDC algorithm is employed to correct depth errors based on 4-beam LiDAR clouds, thereby improving pseudo-LiDAR quality. For pseudo-LiDAR downsampling, a 64-beam point cloud downsampling algorithm is utilized to improve the efficiency of PV-RCNN++ on dense pseudo-LiDAR clouds. Results from experiments on the KITTI 3D dataset indicate that our method substantially boosts detection accuracy across three object categories compared to traditional stereo detection algorithms. Notably, in the task of car detection, it surpasses the state-of-the-art stereo detection algorithm DSGN++ in terms of accuracy.

关键词： Point cloud compression Accuracy Three-dimensional displays Laser radar Clouds Object detection Cameras Stability analysis detection algorithms Autonomous vehicles

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A Study of Attention-Based YOLOv5 for Forest Fire detection

A Study of Attention-Based YOLOv5 for Forest Fire Detection

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Industrial IoT, Big Data and Supply Chain (IIoTBDSC), International Conference on

作者： Zizhen Feng Strand Campus King’s College London London UK

ISBN: (数字)9798331539818

ISBN: (纸本)9798331539825

Fire detection is crucial for safeguarding lives and property. The development of deep learning technology has enabled significant potential in fire detection methods based on image and video analysis. The study proposes an improved fire detection algorithm based on YOLOv5 by incorporating a Coordinate Attention (CA) mechanism to enhance model performance. A dataset of 2,675 annotated flame images was constructed, with the CA module integrated into the C3 module of YOLOv5. Experimental results reveal that the enhanced model achieves a remarkable balance between precision and recall, with the F1 score peaking at 0.67774 near a confidence threshold of 0.13. The model maintains stable performance within the 0.1 to 0.5 confidence range, providing flexibility for practical applications. At higher confidence levels, the model exhibits nearly perfect precision. The integration of the CA mechanism with YOLOv5 significantly boosts fire detection accuracy and efficiency, offering new insights for developing more reliable fire safety systems.

关键词： YOLO Sensitivity Fluctuations Supply chains Fires Forestry Data models detection algorithms Industrial Internet of Things Fire safety

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Enhanced Underwater Object detection in Sonar Images with Range-Setting Embedding and Sliding Window Preprocessing

Enhanced Underwater Object Detection in Sonar Images with Ra...

引用

OCEANS

作者： Ganzorig Baatar Linda Ritzau Divas Karimanzira Thomas Rauschenbach Advanced System Technology (AST) Branch of IOSB Fraunhofer-Institute IOSB Ilmenau Germany

ISBN: (数字)9798331540081

ISBN: (纸本)9798331540098

This paper presents a robust real-time object detection approach utilizing sonar imagery from forward looking sonar systems, aimed at enhancing navigation and obstacle avoidance algorithms for autonomous underwater vehicles (AUVs). Effective autonomous navigation of an underwater vehicle ne-cessitates precise knowledge of the surrounding environment. While cameras are often employed for near field environmental scanning, they encounter significant challenges such as turbidity and light absorption, which can compromise the reliability of visual data. In contrast forward looking sonar provide reliable measurements and detailed environmental information. However, the sonar images pose their own challenges, including low resolution and substantial noise, complicating the accurate identification of visual features. To address these issues, we propose a novel sonar image preprocessing method designed to improve object detection. This method involves systematically moving a predetermined-size window (in meters) across the sonar image, extracting data from various positions to create smaller, fixed-size images. The window size is determined by the sonar device's range setting ensuring that the extracted images will represent a consistent area of the underwater environment. Hence, extracted images consistently represent specific areas of the underwater environment. These preprocessed images are then utilized for training object detection models and as input for detection tasks. This preprocessing step significantly enhances the stability and reliability of target object detection making it compatible with a wide range of existing object detection models.

关键词： Training Visualization Sonar measurements Training data Object detection Sonar navigation Feature extraction Reliability detection algorithms Sonar detection

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Automatic detection of Sentiment Towards Explicit Aspect in Russian Publicism Sentences Using Syntactic Structure

Automatic Detection of Sentiment Towards Explicit Aspect in ...

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Open Innovations Association (FRUCT), Conference of

作者： Anatoliy Poletaev Ilya Paramonov Elena Boychuk P.G. Demidov Yaroslavl State University Yaroslavl Russia

ISBN: (数字)9789526524627

ISBN: (纸本)9798331507411

The paper describes experiments on using the syntactic structure to improve the performance of sentiment detection towards an explicit aspect in Russian sentences. Three methods of syntactic structure usage were evaluated. The first is augmenting sentences from training set by changing their syntactic structure. The second is passing local context subtrees to Interactive Attention Network (IAN). The third is the novel semantic rule-based sentiment detection algorithm based on the previous authors’ work. Sentences augmentation and passing local context did not exceeded the baseline, their macro F-scores were 0.68 and 0.69 respectively. The proposed rule-based algorithm performed almost as well as the baseline method, its macro F-score is 0.70. The ensemble of BERT-SPC and the rule-based algorithm performed with F-score 0.81, which exceeds the baseline by 10 %.

关键词： Training Technological innovation Logistic regression Semantics Neural networks Syntactics detection algorithms

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Traffic Incident detection Algorithm based on Deep Learning: A Statistical Optimization Approach

Traffic Incident Detection Algorithm based on Deep Learning:...

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Electronics and Renewable Systems (ICEARS), International Conference on

作者： Zhao Jing College of Automobile and Traffic Engineering Heilongjiang Institute of Technology Harbin China

ISBN: (数字)9798331509675

ISBN: (纸本)9798331509682

The continuous evolution of smart transportation has led to the vigorous development of research in the field of intelligent vehicle anomaly detection. This study proposes a new traffic incident detection algorithm based on deep learning and combines it with statistical optimization methods for real-time traffic incident monitoring. This model first uses the pre-trained YOLOv5m_c model to detect vehicles and outputs images with vehicle frames from the video stream. At the same time, from the data analysis level, it determines whether there is abnormal behavior between vehicles based on the characteristics of the vehicle frame. The proposed algorithm improves the effect of abnormal events by optimizing the network structure of the YOLOv5 model. Then, starting from the sensor level, the detection method based on the Internet of Vehicles collects and shares vehicle status information in real time. This analysis process fully considers the task offloading and migration strategy in the edge computing environment. Quantitative experimental results show that the proposed algorithm can effectively improve the detection accuracy of traffic incidents and reduce the false alarm rate in real-time traffic data.

关键词： Deep learning YOLO Computational modeling Streaming media Prediction algorithms Real-time systems detection algorithms Anomaly detection Internet of Vehicles Edge computing

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Enhancing Traffic Incident detection Through ADASYN-Attention Fusion: A Comparative Study with RITIS Data

Enhancing Traffic Incident Detection Through ADASYN-Attentio...

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International Conference on Intelligent Transportation

作者： Ruifeng Liu Yuanchang Xie Polichronis Stamatiadis Nathan Gartner Tingjian Ge Department of Computer Science University of Massachusetts Lowell Lowell MA USA Department of Civil and Environmental Engineering University of Massachusetts Lowell Lowell MA USA Department of Civil Engineering Ariel University Israel

ISBN: (数字)9798331505929

ISBN: (纸本)9798331505936

Traffic incidents are a leading contributor to non-recurring congestion and secondary crashes. Each year congestion and crashes together cost the United States over 1 trillion dollars. Once traffic queues are formed, it is difficult to dissipate them and return traffic to normal operations. Therefore, real-time and accurate incident detection plays a critical role in Traffic Incident Management (TIM). This research focuses on highway traffic incident detection. It divides a highway network into short segments and correlates temporal and spatial data from adjacent segments for detecting incidents. Due to incidents being relatively rare compared to normal traffic patterns, we propose a method that combines oversampling with the attention mechanism and use an ablation study to prove its effectiveness in improving supervised incident detection.

关键词： Road transportation Attention mechanisms Costs Accuracy Traffic control Computer crashes Spatial databases Real-time systems detection algorithms Intelligent transportation systems

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Credit Card Fraud detection Algorithm Based on Improved Particle Swarm Optimization

Credit Card Fraud Detection Algorithm Based on Improved Part...

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6th International Conference on Intelligent Computing and Signal Processing (ICSP)

作者： Xun Qiu Weijia Qin Chaoyang Guo Wencheng Mao School Of Computer Science Harbin Engineering University Harbin China College of Intelligent Systems Science and Engineering Harbin Engineering University Harbin China

ISBN: (数字)9798350376548

ISBN: (纸本)9798350376555

With the popularity of mobile payment, the problem of financial fraud is becoming increasingly prominent, and the limitations of traditional anti-fraud methods make it more difficult to fight fraud. Many fraud detection tools lack generalization and cannot provide services for the majority of enterprises. To solve this problem, this paper introduces and compares the performance of a variety of popular meta-heuristic algorithms in the field of feature selection, in order to pursue the best performance and generalization. In addition, this paper improves the particle swarm optimization algorithm by introducing cost-effectiveness, so that it can show better performance in the feature selection problem. In this paper, through a comprehensive evaluation of the system, a variety of performance indicators and confusion matrix are examined to accurately evaluate the performance of the system. In particular, the potential of the system in practical applications is highlighted, and it is expected to achieve significant results in real scenarios. And through the analysis of detailed performance indicators and confusion matrix, the feasibility and effectiveness of the system in solving practical problems and coping with various challenges are highlighted.

关键词： Online banking Metaheuristics Signal processing algorithms Finance Signal processing Feature extraction Credit cards Fraud Particle swarm optimization detection algorithms

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Research on Intelligent Traffic Pedestrian detection Algorithm Based on Improved YOLOv8

Research on Intelligent Traffic Pedestrian Detection Algorit...

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Artificial Intelligence and Computer Engineering (ICAICE), International Conference on

作者： Yurong Yue Xiaoyang Kong Rui Pan Wei Shan College of Physics and Electronic Information Huaibei Normal University Huaibei China Anhui Provincial Key Laboratory of Intelligent Computing and Application Huaibei China

ISBN: (数字)9798331528911

ISBN: (纸本)9798331528928

Aiming at the problem of poor detection of long-distance pedestrians in intelligent traffic scenarios, an improved YOLOv8 pedestrian detection algorithm is proposed. Using YOLOv8 as the basic network, SPD-Conv is embedded before the C2f layer of backbone and after the Conv layer of neck as the key preprocessing step. Experimental results on the public dataset Caltech Pedestrian Dataset show that compared with the baseline algorithm, mAP0.5 is increased by 3%, mAP0.5: 0.95 is increased by 5.8%, and LAMR is reduced by 5.5%. Compared with the popular algorithm, mAP0.5 and mAP0.5: 0.95 are as high as 0.892 and 0.598, respectively, and LAMR is as low as 0.249, which is better than the popular algorithm. The results show that the algorithm achieves a significant improvement in accuracy in pedestrian detection tasks, and effectively reduces the occurrence of missed detection. It can serve intelligent transportation more effectively and reduce the potential safety hazards caused by missed detection.

关键词： Training Adaptation models Technological innovation Pedestrians Accuracy Transportation Feature extraction Hazards Neck detection algorithms

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Improved Fire and Smoke detection Method Based on RT-DETR

Improved Fire and Smoke Detection Method Based on RT-DETR

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International Symposium on Environmentally Conscious Design and Inverse Manufacturing (EcoDesign)

作者： Jiawei Ding Wanjun Yu Shanghai Institute of Technology Shanghai China

ISBN: (数字)9798350363043

ISBN: (纸本)9798350363050

Fire and smoke detection is an important task for preventing early fires and reducing human casualties. Recognizing and classifying fire and smoke instances in various real-life scenarios based on infrared, smoke, and other sensors cannot achieve good accuracy. An improved flame detection algorithm based on RT-DETR is proposed to enhance fire and smoke detection accuracy. The BasicBlock in resnet18 is improved using the shift-wise operator, which changes the spatial location of the features, enabling the model to capture more spatial information. The CCFF in RTDETR is improved using HS-FPN, which enables the fusion of high-level features with low-level features. Finally, the improved RT-DETR model has an mAP value of 61.8% on the fire and smoke detection dataset of robo-flow, which is 1.7 percentage points higher than the original RT-DETR model, and also the number of parameters decreases by 29.0%, which improves the accuracy and achieves a lighter model at the same time.

关键词： YOLO Accuracy Biological system modeling Image edge detection Fires Sensors detection algorithms Informatics

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