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检索条件"任意字段=2024 3rd International Conference on Image Processing, Object Detection, and Tracking, IPODT 2024"

共 128 条记录，以下是81-90 订阅

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A small target detection method based on different scales for unmanned aerial vehicles 3

A small target detection method based on different scales fo...

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3rd international conference on Electrical, Electronics, and Information Engineering, EEIE 2024

作者： Qi, Xiuyuan Wang, Zheng Liu, Zherong Wei, Haojie Hao, Shuang Zhai, Yufei Liang, Qing Liu, Ye University of Electronic Science and Technology of China Chengdu China AVIC Chengdu Aircraft Design & Research Institute Chengdu China

ISBN: (纸本)9781510688179

With the continuous iteration and development of computer vision technology, intelligent applications and devices centered around this technology are gradually playing an increasingly important role in human daily life and military operations. Among these, technologies based on object recognition and detection are widely applied in fields such as robotics, drones, and aircraft combat. These areas require precise localization information provided by object detection technology to achieve functions like navigation or attack. However, due to the characteristics of the targets being detected, their scale changes according to their distance, resulting in significant scale variations in images for the same target. Timely detection of distant targets is crucial for aircraft control and early warning, making small target recognition a key factor limiting the widespread application of recognition algorithms. To address this issue, this paper processes the original images based on color space level, global level, and pixel level, proposing a small target recognition method for RGB images. This method involves separately processing different color spaces of the RGB image, followed by information fusion, analysis of the entire image and local regions, and pixel-level filtering in local areas. Experimental results demonstrate that our algorithm can accurately recognize targets of varying sizes and observation angles. © The Authors.

关键词： Information fusion

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Improved UAV-to-Ground Small object detection Algorithm Based on Yolov5s 3rd

Improved UAV-to-Ground Small Object Detection Algorithm Base...

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3rd international conference on Machine Learning, Cloud Computing and Intelligent Mining, MLCCIM 2024

作者： Luo, Ling Wu, Zhang Han, Wei Sun, Xuefei Bai, Tingting Army Engineering University of PLA Jiangsu Nanjing210001 China

ISBN: (纸本)9789819624676

Small objects make up a high proportion of Unmanned aerial vehicle (UAV) images, but the existing detection algorithms have problems such as low detection accuracy and high leakage rate. To improve small object detection, an improved YOLOv5s model is proposed in this paper. In the Neck Network of the YOLOv5s model, a channel attention module attention (CBAM) mechanism is added. By adding CBAM after each CSP2_1 module and before the convolution operation, the model can automatically ignore non-critical information and focus highly on key features in the image. The improved YOLOv5s model was evaluated on the VisDrone2019-DET dataset and its performance was compared with YOLOv5s and YOLOv8s. The YOLOv5s-CBAM model has improved mAP_0.5 by 2.2% and mAP_0.5:0.95 by 1.2%. The loss convergence speed is faster and the stability is better. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： object tracking

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Generalized DINO: DINO via Multimodal Models for Generalized object detection 24

Generalized DINO: DINO via Multimodal Models for Generalized...

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3rd international conference on Computer, Artificial Intelligence and Control Engineering, CAICE 2024

作者： Yuen, Kashing Zou, Jianpeng Uchida, Kaoru Hosei University Tokyo Japan

ISBN: (纸本)9798400716942

Referring Expression Comprehension (REC) is a task in the realm of vision and language, aiming to identify objects in images based on provided descriptions. Classic REC methods, however, face challenges in handling expressions involving multiple targets or empty scenarios. In this paper, we study the limitations of existing REC methods, particularly in the context of Generalized Referring Expression Segmentation (GRES). In response, we propose Generalized DINO, a model that extends Transformer-based detectors by incorporating Region-image Cross Attention (RIA) and Region-Language Cross Attention (RLA) mechanisms. This approach enables the detector to support arbitrary numbers of target object detection, overcoming the constraints of traditional REC methods. Comprehensive experiments on widely-used datasets such as RefCOCO/+/g and the GRES benchmark gRefCOCO showcase the superior performance of Generalized DINO in GRES tasks. The model outperforms even the robust RELA model, demonstrating a significant stride in handling expressions with multiple targets or empty scenarios. Our findings underscore the efficacy of Generalized DINO in enhancing the robustness and flexibility of REC models, contributing to multimodal information processing. The model's ability to handle complex language expressions involving multiple objects positions it as a valuable asset in applications like human-computer interaction and visual question answering. © 2024 ACM.

关键词： Human computer interaction

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Smart Monitoring and Diagnostics for Fault detection in Power Plant Equipment 3

Smart Monitoring and Diagnostics for Fault Detection in Powe...

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3rd IEEE international conference on Distributed Computing and Electrical Circuits and Electronics, ICDCECE 2024

作者： Kala, S. Padma Kumar S, Pradeep Meerimatha, G. Almusawi, Muntather Gurumoorthy, Sasikumar Dept Of Cse Tamilnadu Chennai India Nitte Meenakshi Institute Of Technology Department Of Ece Bengaluru India Department Of Eee Ananthapuramu India College Of Technical Engineering The Islamic University Department Of Computers Techniques Engineering Najaf Iraq School Of Engineering And Technology Dhanalakshmi Srinivasan University Computer Science And Engineering Tamil Nadu Tiruchirappalli India

ISBN: (纸本)9798350318609

In recent years, Fault Diagnosis and Identification (FDI) has grown significantly. Generally used methodology in these types of frameworks include model-based approaches, fault approaches, and pattern recognition techniques. However, it was essential to society's energy supply, therefore maintaining its safe and effective functioning is of utmost importance. This research used sophisticated Fault detection and Diagnosis (FDD) methods to diagnose the problems in contemporary power plants in order to decrease maintenance shutdowns and expenses. Therefore, this research applied deep learning to image processing using efficient Long Short-Term Memory (LSTM)-based methods for object recognition and categorization. Using the observed frequency as input, the trained LSTM is utilized to detect power variations in real time. Control devices, such as synchronous generators and Energy Storage Systems (ESSs), was maintain a steady frequency by using the detected power variations. According to the results, the suggested approach demonstrated superior performance in terms of LG faults, with 0.427%, LLG faults of 0.800%, LGG faults of 0.186 %, and LL faults of 0.741%. Compared with existing methods namely Sparrow Search Algorithm (SSA) and Deep Long Short-Term Memory (LSTM) it shows that suggested methods are achieved better performances with less faults respectively. © 2024 IEEE.

关键词： Long short-term memory

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A Comprehensive Approach to Enhancing Science Practical Education using AI, ML, image processing, and NLP for Secondary Education 3

A Comprehensive Approach to Enhancing Science Practical Educ...

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3rd international conference on Automation, Computing and Renewable Systems, ICACRS 2024

作者： Ranasinghe, K.P.P.U. Wickramarachchi, D.C. Ranasinghe, J.S. Karunarathna, W.P.D.B. Rajapaksha, Samantha Jayasinghearachchi, Vishan Department of Information Technology Sri Lanka Institute of Information Technology Malabe Sri Lanka Sri Lanka Institute of Information Technology Malabe Sri Lanka Sri Lanka Institute of Information Technology Malabe Sri Lanka

ISBN: (纸本)9798331532420

Practical science education is crucial for developing students’ understanding of core scientific concepts, yet many secondary students face challenges due to inadequate school resources. This often results in lower academic performance and diminished interest in science. To address these issues, we propose the development of an innovative online platform designed to enhance science practical education by integrating four advanced technologies: emotion detection, hand gesture recognition, object identification, and chatbot support. This platform offers an interactive and engaging approach to learning by leveraging technology to overcome the limitations of traditional classroom resources. The emotion detection feature monitors students’ emotional responses as they interact with science-related practical questions. By analyzing emotional cues such as confusion or frustration, the platform delivers personalized content to address areas where students struggle. The hand gesture recognition feature presents multiple-choice questions and allows students to respond by showing the number of their answer choice with their fingers, promoting quick thinking and improving calculation speed. object identification enables students to upload images of objects from the science curriculum, which the platform then identifies and explains, helping students relate theoretical knowledge to real-world examples. Additionally, the platform’s chatbot provides real-time support, answering students’ questions on practical topics and guiding them through difficult concepts. Through this comprehensive approach, our platform bridges the gap in science practical education by providing students with an accessible, personalized, and interactive learning experience. This solution not only enhances their understanding of scientific principles but also fosters academic success and a deeper engagement with the subject matter. © 2024 IEEE.

关键词： Students

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CT image Data Enhancement of Pulmonary Nodules Based on Deep Learning Technology and Digital image processing Technology 24

CT Image Data Enhancement of Pulmonary Nodules Based on Deep...

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3rd international conference on Computer, Artificial Intelligence and Control Engineering, CAICE 2024

作者： Gu, Qingyu Palaoag, Thelma D Harbin University Harbin China University of the Cordilleras Baguio City Philippines

ISBN: (纸本)9798400716942

With the continuous progress of modern society, people's lives have undergone great changes, and the rich material life has improved people's quality of life, and also extended the overall life expectancy of human beings, but pulmonary diseases still pose a serious threat to human health. In order to improve the object detection performance of pulmonary nodules, this study proposed an enhancement strategy for the CT image data of pulmonary nodules to achieve the detection and diagnosis efficiency of pulmonary nodules. Firstly, the single-channel extraction of the original pulmonary nodule CT image data can not only reduce the memory occupation of the data, but also improve the training and inference time, and secondly, the single-channel grayscale image is processed by adaptive histogram to improve the image contrast. In order to further improve the details of the image, the linear mapping method in dynamic range compression is adopted to realize the image enhancement and reduce the amount of data, which is conducive to the storage and transmission of the image. Finally, the wavelet transform method is used to further enhance the data and improve the details of the image. In the experimental results of Fast R-CNN model, the precision of the CT image data of pulmonary nodules processed by data enhancement strategy was increased by 10.08 percentage points, the recall rate was increased by 9.41 percentage points, and the mAP value was increased by 10.10 percentage points. In the experimental results of the Fast-RCNN-CBAM model, the precision is improved by 12.26. The recall rate increased by 7.22 percentage points, and the mAP value increased by 7.67 percentage points. Through the above experimental results, it is shown that the data enhancement strategy proposed in this paper can effectively improve the detection performance of the object detection model on the CT image data of pulmonary nodules, and improve the diagnostic effectiveness of pulmonary nodule lesions. ©

关键词： Computerized tomography

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Hardware acceleration design and implementation of YOLOv3-tiny based on ZCU102 platform 3

Hardware acceleration design and implementation of YOLOv3-ti...

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3rd international conference on Electronic Information Engineering, Big Data, and Computer Technology, EIBDCT 2024

作者： Zhao, Yixuan Shi, Tianjie Liu, Feiyang Tian, Jing Xi'an Aeronautics Computing Technique Research Institute Xi'an China

ISBN: (纸本)9781510680449

Convolutional Neural Networks (CNNs) have been widely applied in the field of image recognition. To meet the extensive computational demands of CNNs, GPUs or other intelligent computing hardware are commonly employed for data processing. FPGA supports parallel computing and is characterized by programmability, high performance, low energy consumption, and strong stability. This paper provides a detailed introduction to Xilinx's FPGA development tool software, Vitis AI, designed for artificial intelligence, along with the process of generating and implementing FPGA hardware logic for deep learning algorithms. The functionality and performance were verified through FPGA experimental platforms. We used a 16-bit fixed-point method to segment neural network tasks and preprocess data, thereby reducing hardware resource consumption. By employing the ZCU102 development platform to accelerate the YOLOv3-Tiny algorithm, we achieved target object detection and recognition. The power consumption was only 9.9% of GPU Quadro K6000, and compared with other YOLOv3-tiny implementations on ZCU102, the detection speed was increased by 5.7 times. © COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.

关键词： Computation theory

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Faster-RCNN in Human Detecting on Thermal images 3rd

Faster-RCNN in Human Detecting on Thermal Images

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3rd international conference on Advances in Information and Communication Technology, ICTA 2024

作者： Anh, Le Tuan Ngan, Tran Thi Dung, Vu Viet Luc, Do Dinh Nguyen, To Huu Thai Nguyen University of Information and Communication Technology Thai Nguyen Viet Nam International School Vietnam National University Hanoi Viet Nam

ISBN: (纸本)9783031809422

By the development of Artificial Intelligence, many applications of deep learning and computer vision algorithms in object detection on color images were introduced. The obtained results get higher accuracy than other classical methods. However, detecting objects on thermal images has not received much research attention. In this paper, we focus on researching and proposing the use of Faster-RCNN deep learning model in order to detect people on thermal images. The proposed model consists of two phases: Data processing and model applying. The experiments on a thermal image dataset, namely as Deep Thermal Outdoor, are performed to evaluate performance of the proposed model and compare it to YOLO and SSD models. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Contrastive Learning

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3rd international conference on Artificial Intelligence and Knowledge processing, AIKP 2023

3rd International Conference on Artificial Intelligence and ...

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3rd international conference on Artificial Intelligence and Knowledge processing, AIKP 2023

ISBN: (纸本)9783031686160

The proceedings contain 28 papers. The special focus in this conference is on Artificial Intelligence and Knowledge processing. The topics include: How Do Senior Secondary Level Students and Their Teachers Perceive Artificial Intelligence and Its Implementation? An Exploratory Study;anomalous Sound Pattern detection for Machine Health Monitoring;performance Evaluation of Various Machine Learning Algorithms for Lung Cancer Prediction Using Demographic Data;enhancing Stock Portfolio Optimization Based on a Hybrid Approach Using Artificial Bee Colony Optimization and Firefly Optimization;Enhancing Yarn Quality in the Cotton Industry: AI- Based Nep detection for Improved Manufacturing Processes;breast Cancer Diagnosis from Ultrasonic image and Histopathology image Using Deep Learning Approach;Advancing Time Series Forecasting: LSTM Networks with Multiple Attention Mechanisms;trajectory tracking and Navigation Model for Autonomous Vehicles Using Reinforcement Learning;quantum Graph Neural Networks Based Protein-Ligand Classification;comparative Analysis on Speech Driven Gesture Generation;enhancing Deep Learning: Leveraging Skip Connections and Memory Efficiency;quality-Based Decision-Making Using image processing for Supply Chain Management;enhancing Endometrial Tumor detection: Early Diagnosis with Advanced Vision Transformer Architecture;sweetSight: A Deep Convolutional Neural Network Approach for Automatic Categorization of Bengal Sweets;a Systematic Review: How Computer Vision is Transforming Agriculture in Economic Growth;automatic Conversion of Broadcasted Football Match Recordings to Its 2D Top View;measuring the Vehicle-in-Motion, Density and Allocation of Traffic Signal Using Transfer Learning;Ensemble Model of VGG16, ResNet50, and DenseNet121 for Human Identification Through Gait Features;Performance of Sentiment Analysis APIs on Political Opinion Polling;summarization of Telugu Text Discourses;crowd-Sourced Supervisors for the Automatic Invigilation of Online

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Deep Learning-Based Brain Tumor Segmentation—An Overview 1

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3rd international conference on Artificial-Business Analytics, Quantum and Machine Learning: Trends, Perspectives, and Prospects, Com-IT-Con 2023

作者： Kataria, Jyoti Panda, Supriya P. Manav Rachna International Institute of Research and Studies Haryana Faridabad India

ISBN: (数字)9789819748600

ISBN: (纸本)9789819748594

Brain tumor segmentation is a subdivision of study that focuses on creating a precise delineation of brain tumor regions using magnetic resonance imaging (MRI). Nowadays, deep learning (DL) algorithms have shown remarkable growth in addressing a range of computer vision problems like semantic segmentation, image classification, and object detection. Applying DL techniques to electronic health records can yield useful insights that can be used to improve patient risk score systems or identify the onset of a brain tumor. Deep learning techniques can also facilitate the effective processing and balanced assessment of the vast volumes of MRI-based image data. This paper presents an overview of the traditional and deep learning techniques used for brain tumor segmentation (BTS) and brain tumor classification (BTC). Further, a detailed analysis of the existing surveys was carried out to determine the overall impact of each survey. In addition, this study discusses the deep learning-based methods with their respective classification/segmentation, features, pre-processing, and outcomes. Finally, this study not only examines the prior research but identifies potential future research directions. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Semantic Segmentation

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