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检索条件"任意字段=International Conference on Estimation Detection and Information Fusion ICEDIF"

共 1408 条记录，以下是1-10 订阅

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Proceedings of 2015 international conference on estimation, detection and information fusion, icedif 2015

Proceedings of 2015 International Conference on Estimation, ...

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international conference on estimation, detection and information fusion, icedif 2015

ISBN: (纸本)9781479964178

The proceedings contain 85 papers. The topics discussed include: fault detection and noise variance identifier with cooperation fault-torlerance for multisensor system;optimal filtering for multi-rate systems with one-step auto-correlated noises;new variable mode QRD adaptive filtering algorithm for acoustic echo cancellation;an optimized travelling time estimation mechanism for minimizing handover failures from cellular networks to WLANs;partial discharge type recognition for transformers based on fisher discriminant method;object recognition based on reconstruction of light field;robust measurement fusion steady-state Kalman predictor for multisensor uncertain system;robust centralized fusion steady-state Kalman filter for multisensor uncertain systems;an approach for space registration based on support vector machine;nonlinear model predictive control with neuro-fuzzy aiding for 3 DOF helicopter;and performance of two-dimensional LV distribution.

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MoPoSD: Monocular 3D Object Detector with Potential for Specialized Exploration in Depth 16

MoPoSD: Monocular 3D Object Detector with Potential for Spec...

引用

2024 16th international conference on Graphics and Image Processing, ICGIP 2024

作者： Lin, Jiayu Liu, Songyan Wang, Yang School of Information Science and Engineering Yunnan University China

ISBN: (数字)9781510688780

ISBN: (纸本)9781510688773

Monocular 3D object detection is a critical task in autonomous driving and robotic vision. Recently, center point-based monocular 3D object detection methods have achieved a balance between speed and accuracy. However, these methods face challenges in depth information acquisition and performance potential, often relying heavily on local information. To address these challenges, we propose a novel monocular 3D object detection model named MoPoSD (Monocular 3D Object Detector with Potential for Specialized Exploration in Depth). MoPoSD utilizes an attention mechanism tailored for object detection and incorporates an encoder-decoder architecture. Our model introduces an efficient encoder, TIE(Targeted Interaction Encoder), which updates features in an interlaced manner to achieve lightweight processing. Additionally, we propose a branch module for depth information estimation called DAFM(Depth Attention fusion Module). This module leverages multi-scale feature layer pyramid fusion and attention mechanism to improve depth estimation accuracy and the model's robustness when handling objects at varying distances. Furthermore, our proposed decoder also introduces an advanced group-decision mechanism to enhance the supervision signal of the training target, named MSMD(Multiple Sets of Matching Decoders). Extensive experiments demonstrate that MoPoSD achieves state-of-the-art performance on the KITTI benchmark using monocular images as input without requiring additional dense depth annotations. © 2025 SPIE.

关键词： Decoding

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RAFDet: A Novel Camera-Radar fusion Framework for Robust 3D Object detection in Autonomous Driving

RAFDet: A Novel Camera-Radar Fusion Framework for Robust 3D ...

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2025 IEEE international conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Cao, Xingjian Wang, Ping Zhang, Zhitao Tu, Huizhao Chen, Yong Liang, Zhenbao College of Electronic and Information Engineering Tongji University Shanghai China College of Transportation Tongji University Shanghai China Co. Ltd. Ningbo China

ISBN: (纸本)9798350368741

Accurate and reliable 3D object detection is crucial for autonomous driving, normally achieved using camera-only or camera-LiDAR fusion methods based on BEV (Bird's Eye View) perspective. However, visual perception through cameras alone faces significant challenges, such as ambiguous depth estimation and poor performance in low-light, while camera-LiDAR fusion, though robust, are expensive on deployment and have limitations in dust and fog weather conditions. To address these issues, we propose RAFDet (Radar-Assisted fusion detection), a novel multi-modality fusion framework integrating camera and radar data for enhanced 3D detection. RAFDet utilizes radar RF (Radio Frequency) images to enrich spatial details, boosting fusion effects, while radar point clouds provide precise depth information to rectify visual depth predictions. In addition, the Edge-Awareness Feature Enhancement mechanism compensates for the sparsity of radar points, further refining depth estimation and detection accuracy. Our extensive experiments demonstrate the superiority of RAFDet over current methods, highlighting its potential for autonomous driving systems. Key results show significant improvements in depth prediction and overall performance metrics, validating the effectiveness of our approach. Our data collection and testing in real traffic scenarios also reflects the robustness, accuracy, and cost-effectiveness of RAFDet for 3D object detection in autonomous driving. © 2025 IEEE.

关键词： 3D object detection Autonomous driving Camera-radar fusion Depth-rectification view transformation Edge-awareness feature enhancement Radar RF image

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Implementation of Radar-Camera fusion for Efficient Object detection and Distance estimation in Autonomous Vehicles 3

Implementation of Radar-Camera fusion for Efficient Object D...

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3rd international conference on Intelligent and Innovative Technologies in Computing, Electrical and Electronics, IITCEE 2025

作者： Prajwal, R. Pranav Sharma, N. Tejasvi, P.C. Mithun, T.P. Reddy, B. Roja R V College of Engineering Dept of Ete Bengaluru India

ISBN: (纸本)9798331515911

Autonomous vehicles depends on various sensors to accurately perceive their surroundings, ensuring safe and efficient navigation. These sensors include radar, lidar, cameras, and ultrasonic sensors, each offering unique strengths. Radar provides precise distance and velocity measurements in diverse conditions, while cameras offer detailed visual information for object recognition. State-of-the-art perception systems increasingly utilize sensor fusion to combine these strengths, addressing the limitations of individual sensors and enhancing overall vehicle perception capabilities. Challenges in radar-camera fusion include misalignment of sensor data due to differing spatial resolutions and difficulties in effectively integrating radar's sparse data with dense image data. Existing models like CDMC and RODNet struggle with limited precision or recall due to suboptimal fusion strategies. This work employs the Radar Multiple-perspectives Convolutional Neural Network (RAMP-CNN) architecture, which leverages the radar and camera data fusion through Conventional Neural Network (CNN) to improve perception. Radar data preprocessing involves steps like 3D Fast Fourier Transform, while image preprocessing includes gamma correction and noise reduction. The fusion process combines 2D image proposals with radar data, significantly enhancing object detection and distance estimation accuracy. Simulation results demonstrate the efficacy of this fusion approach. Performance metrics such as Precision, Recall, F1-Score, Mean Squared Error (MSE), Mean Absolute Error (MAE), R2 Score, and Mean Intersection over Union (Mean IoU) highlight the model's effectiveness. While precision is high, recall indicates room for improvement. Low error metrics suggest accurate distance estimations, and the R2 score confirms the model's strong explanatory power. This fusion method represents a significant advancement in autonomous vehicle perception by increasing Average Recall (AR) by 3% and Average Preci

关键词： Noise abatement

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Research on UAV night object detection based on parallel fusion neural network 4

Research on UAV night object detection based on parallel fus...

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4th international Computational Imaging conference, CITA 2024

作者： Bo, Zhou Gui, Fu Hongyu, Chu You, Wang Xinyu, Zhu Avionics and Electricity College Civil Aviation Flight University of China 618307 China College of Information Engineering Southwest University of Science and Technology 621000 China

ISBN: (纸本)9781510688834

In nighttime conditions, the accuracy of object detection algorithms used in UAVs is often compromised by factors like noise and poor lighting. To tackle this issue, a parallel fusion neural network is designed in this article for Drone-view object detection in low-light *** approach begins by running the light enhancement network and the object detection network simultaneously. The light enhancement module utilizes Zero-DCE (Zero-Reference Deep Curve estimation), which doesn't require paired or unpaired data during training;instead, it relies on a well-defined non-reference loss function. For the detection component, we employ a lightweight YOLOv5 *** optimize our model, we introduce the Efficient Multi-Scale Conv Plus (EMSCP), which improves feature maps through multi-scale convolution leveraging specialized multi-scale deep convolutional blocks. Additionally, we incorporate a Large Separable Kernel Attention (LSKA) module to emphasize key features. The LSKA mechanism captures extensive contextual information from the image using large, separable convolutional kernels and spatially expanding convolutions. It generates an attention map that weighs the original features, thereby enhancing the network's focus on critical aspects and boosting overall model *** conducted on two nighttime datasets indicate that our method significantly outperforms traditional approaches in detection *** method holds significant promise for nighttime UAV visual monitoring. © 2025 SPIE.

关键词： Drones

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Multi-KeyPoint Depth fusion for Monocular 3D Object detection 24

Multi-KeyPoint Depth Fusion for Monocular 3D Object Detectio...

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2nd international conference on Communication Networks and Machine Learning, CNML 2024

作者： Zhang, Shuiqiang Sun, Mingtai Cui, Xiaoshuo Huang, Yuan School of Engineering Huzhou University Zhejiang Huzhou China School of Science Huzhou University Zhejiang Huzhou China

ISBN: (纸本)9798400711688

Directly estimating the position and pose of objects in a 3D coordinate system from monocular images is an extremely challenging task. Many existing methods typically rely on direct regression or geometric projection techniques to obtain a single depth value for the target, but these approaches often struggle to provide sufficiently accurate depth estimates in complex scenes. To address this issue, we propose an improved 3D object detection method that enhances depth estimation accuracy by fusing depth information from multiple keypoints. Specifically, the method first predicts multiple corner points and surface center points of the 3D bounding box. Then, based on the image heights and physical heights of these keypoints, multiple depth values are computed through geometric projection. These depth values are subsequently fused with the depth information directly regressed by the depth branch to achieve more reliable depth estimation. Additionally, to improve the model’s capability in detecting objects of varying scales, we introduce a multi-scale attention enhancement module into the feature extraction network. Finally, experimental results on the KITTI dataset demonstrate that the proposed method significantly improves the performance of 3D object detection. © 2024 Copyright held by the owner/author(s).

关键词： Feature extraction

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MVANet: Multi-Stage Video Attention Network for Sound Event Localization and detection with Source Distance estimation

MVANet: Multi-Stage Video Attention Network for Sound Event ...

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2025 IEEE international conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Hong, Hengyi Wang, Qing Wei, Ruoyu Cai, Mingqi Fang, Xin University of Science and Technology of China Hefei China iFlytek Research Hefei China

ISBN: (纸本)9798350368741

Sound event localization and detection with source distance estimation (3D SELD) involves not only identifying the sound category and its direction-of-arrival (DOA) but also predicting the source's distance, aiming to provide full information about the sound position. This paper proposes a multistage video attention network (MVANet) for audio-visual (AV) 3D SELD. Multi-stage audio features are used to adaptively capture the spatial information of sound sources in videos. We propose a novel output representation that combines the DOA with distance of sound sources by calculating the real Cartesian coordinates to address the newly introduced source distance estimation (SDE) task in the detection and Classification of Acoustic Scenes and Events (DCASE) 2024 Challenge. We also employ a variety of effective data augmentation and pre-training methods. Experimental results on the STARSS23 dataset have proven the effectiveness of our proposed MVANet. By integrating the aforementioned techniques, our system outperforms the top-ranked method we used in the AV 3D SELD task of the DCASE 2024 Challenge without model ensemble. Codes are available at https://***/Hong-Hengyi/MVANet-SELD. © 2025 IEEE.

关键词： audiovisual fusion multi-stage attention Sound event localization and detection source Cartesian coordinates

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DeepMatch: Navigating the Complexities of Underwater Textures for Enhanced Keypoint Matching

DeepMatch: Navigating the Complexities of Underwater Texture...

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2025 IEEE international conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Zhang, Sainan Liu, Tong Wang, Zhe Yu, Zhibin Zheng, Bing Faculty of Information Science and Engineering Ocean University of China Qingdao China Key Laboratory of Ocean Observation and Information of Hainan Province Sanya Oceanographic Institution Sanya China

ISBN: (纸本)9798350368741

Driven by demands for oceanic exploration, advancements in 3D visual tasks based on video frames are essential. Keypoint matching, essential for camera pose and motion, is hindered by the unique challenges of underwater imagery, such as sparse and repetitive textures. To tackle these issues, we introduce the DeepMatch framework, tailored for aquatic environments. It comprises two main components: a feature extraction network with a multi-level feature fusion module for enhanced keypoint detection in sparse textures, and an attention-based graph neural network with relative position encoding to correct matches in repetitive texture areas. Our experiments demonstrate that DeepMatch surpasses traditional and learning-based methods in underwater image matching, providing a robust solution for accurate camera pose estimation. © 2025 IEEE.

关键词： attention aggregation camera pose estimation Keypoint detection keypoint matching

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15th EAI international conference on Bio-inspired information and Communications Technologies, BICT 2024

15th EAI International Conference on Bio-inspired Informatio...

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15th EAI international conference on Bio-inspired information and Communications Technologies, BICT 2024

ISBN: (纸本)9783031815980

The proceedings contain 16 papers. The special focus in this conference is on Bio-inspired information and Communication Technologies. The topics include: Optimizing Drug Delivery Strategies by Pathway Analysis for Waveform Modulation-Based Molecular Communication;Enhancing DNA-Based IoBNT Throughput and Reducing Congestion with Yin-Yang Coding;a Molecular Communication Model Driven by Magnetic Field Force;CDM Based on Izhikevich Neuron Model;multi-user Diffusive Molecular Communication Systems with A Passive Relay Node: Receiver Design and Performance Analysis;machine Learning-Based detection Time estimation for Molecular Communication;taming Signal-Dependent Counting Noise with Machine Learning for Molecular Communication;energy-Efficient Transmitter Creation in Molecular Communication;a Method for Determining Relay Node Location in Molecular Communication;glycemic Oscillation Decomposition-Based Personalized Blood Glucose Prediction with Continuous Glucose Monitoring;A Compact Bio-inspired Microstrip Antenna (Bi-MPA) for Medical Microwave Imaging Applications;Image Encryption and Decryption Algorithm Based on DNA Sequence: Performance Analysis of Channel fusion Processing;Optimizing the Classification of SSVEP Signals in Brain-Computer Interfaces: A Novel Sliding Window Data Segmentation Method Based on Weighted Voting Mechanism;spontaneous Motion of Nanorobots Inspired Computational Technology for Tumor Boundary Exploration.

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Efficient Infrared Image Super-Resolution Reconstruction via Guided Filter Coefficients estimation with Parallax Attention Mechanism

Efficient Infrared Image Super-Resolution Reconstruction via...

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2025 IEEE international conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Wu, Qingyao Chen, Bosheng Li, Chen Tu, Xiaotong Ding, Xinghao Huang, Yue Key Laboratory of Multimedia Trusted Perception and Efficient Computing Ministry of Education of China Xiamen University 361005 China National Key Laboratory of Infrared Detection Technologies Shanghai China

ISBN: (纸本)9798350368741

Due to the spectral range mismatch between the images, building an efficient infrared (IR) image super-resolution algorithm suitable for embedded devices remains a significant challenge. Given that visible images possess more abundant high-frequency information compared to infrared images, we utilize the visible light to guide infrared image super-resolution reconstruction. Specifically, we transfer the reconstruction task to a guided filter learning process, whose coefficients are estimated by joint learning of visible and infrared image to complete the reconstruction through homologous constraints. In order to efficiently predict guided filter coefficients, we design a lightweight network which incorporates reparameterized differential convolution blocks and a feature fusion strategy. Striving to enhance the fusion strategy performance, we utilize parallax attention mechanism to solve the non-pixel registration problem between infrared and visible images. Extensive experiments on two challenging IR image datasets show that our method performs SOTA in terms of PSNR, SSIM and LPIPS as compared to current state-of-the-art approaches while showing its effectiveness and practicality in the edge platform of RK3588. © 2025 IEEE.

关键词： attention mechanism Image reconstruction infrared image lightweight

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