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共 18083 条记录，以下是91-100 订阅

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Artificial Intelligence Algorithms for robotic Harvesting of Agricultural Produce 1

Artificial Intelligence Algorithms for Robotic Harvesting of...

引用

1st international conference on AIML-Applications for Engineering and Technology, ICAET 2025

作者： Kolhalkar, Nilesh R. Pandit, Anupama A. Kedar, Shridhar Ashok Yedukondalu, G. MKSSS's Cummins College of Engg. For Women Department of Mechanical Engineering MH Pune India Department of Computer Science & Engg. Pune MH Pune India Koneru Lakshmaiah Education Foundation Vaddeswaram Department of Mechanical Engineering A.P. Vijayawada India

ISBN: (纸本)9798350355611

robotic harvesting of fruits and vegetables is an advanced technology that leverages robotics, Artificial Intelligence, and Machine vision to harvest the fruits autonomously from plants or trees. This technology aims to address labor shortages, enhance efficiency, reduce costs, and minimize damage to the fruit during harvesting. AI algorithms for fruit detection and harvesting are increasingly used in agricultural automation to improve efficiency and accuracy. The accuracy of detection algorithms in fruit detection and harvesting can differ reliant on various factors, including the type of algorithm used, the quality and diversity of the training data, the complexity of the environment, and the specific fruits being targeted. Advanced control algorithms integrated with image processing ensure that the robotic arm moves smoothly and accurately, minimizing the risk of bruising or damaging the fruit. Soft robotics and adaptive gripping technologies are discussed in the paper which can handle delicate fruits like grapes, without applying excessive force. Machine vision integrated robot arm with novel gripper and cutter for harvesting cluster fruit like grapes is reported in the paper. Case studies of agricultural robots for Orchards, Greenhouses and Field Crops are discussed with detailed analysis along with challenges, future trends and innovations. © 2025 IEEE.

关键词： Machine vision

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PDTrack: Progressive Distance Association for Multiple Object Tracking

PDTrack: Progressive Distance Association for Multiple Objec...

引用

2025 IEEE international conference on Acoustics, Speech, and signal processing, ICASSP 2025

作者： Liu, Yeqiang Li, Weiran Ding, Yanhao Li, Zhenbo College of Information and Electrical Engineering China Agricultural University Beijing100083 China National Innovation Center for Digital Fishery Ministry of Agriculture and Rural Affairs Beijing100083 China Key Laboratory of Smart Farming Technologies for Aquatic Animal and Livestock Ministry of Agriculture and Rural Affairs Beijing100083 China

ISBN: (纸本)9798350368741

Multiple Object Tracking (MOT) has made significant progress in recent years. However, it still faces challenges such as frequent ID switches, trajectory fragmentation, and tracking losses in high-density pedestrian scenarios. To address these issues, we optimized the association algorithm based on the tracking by detection paradigm and proposed a progressive distance association matching algorithm (PDTrack). By using trajectory boxes gradually and orderly for association, we reduce computational costs. Additionally, we introduced the Disambiguation Model and Reassociation Model to reduce erroneous tracking results and avoid missed tracking objects, ensuring the integrity of the trajectories. Experimental results demonstrate that our proposed method achieves higher accuracy and fewer identity switches compared to other mainstream tracking algorithms, effectively enhancing multiple object tracking performance. © 2025 IEEE.

关键词： Computer vision Data Association Multiple Object Tracking Tracking by Detection

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DEFormer: DCT-driven Enhancement Transformer for Low-light Image and Dark vision

DEFormer: DCT-driven Enhancement Transformer for Low-light I...

引用

2025 IEEE international conference on Acoustics, Speech, and signal processing, ICASSP 2025

作者： Yin, Xiangchen Yu, Zhenda Gao, Xin Sun, Xiao Institute of Artificial Intelligence Hefei Comprehensive National Science Center University of Science and Technology of China Hefei230601 China Anhui University Institute of Artificial Intelligence Hefei Comprehensive National Science Center Hefei230601 China School of Vehicle and Mobility Tsinghua University Beijing100084 China School of Computer Science and Information Engineering Hefei University of Technology Hefei230009 China

ISBN: (纸本)9798350368741

Low-light image enhancement restores the colors and details of a single image and improves high-level visual tasks. However, restoring the lost details in the dark area is still a challenge relying only on the RGB domain. In this paper, we delve into frequency as a new clue into the model and propose a DCT-driven enhancement transformer (DEFormer) framework. First, we propose a learnable frequency branch (LFB) for frequency enhancement contains DCT processing and curvature-based frequency enhancement (CFE) to represent frequency features. Additionally, we propose a cross domain fusion (CDF) to reduce the differences between the RGB domain and the frequency domain. Our DEFormer has achieved superior results on the LOL and MIT-Adobe FiveK datasets, improving the dark detection performance. © 2025 IEEE.

关键词： Discrete Cosine Transform Frequency Learning Low-light Image Enhancement Transformer

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QSVG: Quaternion vision Graph Neural Network with Structure Inference for Texture Classification 16

QSVG: Quaternion Vision Graph Neural Network with Structure ...

引用

16th international conference on signal processing Systems, ICSPS 2024

作者： Wen, Haiyuan Huang, Guoheng Cheng, Lianglun Liao, Xianglian Wu, Shiting Yuan, Xiaochen Zhong, Guo School of Computer Science and Technology Guangdong University of Technology Guangzhou510006 China Huizhou Boluo Power Supply Bureau Guangdong Power Grid Co. Ltd. Huizhou China Faculty of Applied Sciences Macao Polytechnic University China School of Information Science and Technology Guangdong University of Foreign Studies Guangzhou510006 China

ISBN: (纸本)9781510689251

In the realm of texture classification, while traditional approaches like Bag of Words (BoW) and Convolutional Neural Networks (CNNs) are commonly used, recent research highlights the remarkable flexibility offered by Graph Neural Networks (GNNs). Specifically, GNNs can effectively represent image blocks as one-dimensional multi-channel node features from three-dimensional data, interconnecting node feature channels. However, existing methods tend to process channels separately during graph convolution, overlooking critical inter-channel connections that are integral to capture intricate relationships between texture features, potentially limiting the effectiveness of GNNs in texture classification. Moreover, textures contain vital global information essential for classification performance, but GNNs tend to focus on information from similar neighboring nodes, which can restrict their capability to capture the global feature of the entire image during data processing. To address the aforementioned challenges, we propose a novel GNN architecture called Quaternion Structure inference vision Graph neural network (QSVG) for texture classification, which combines quaternions and structure inference. Unlike previous approaches, QSVG comprises two key components: first, Quaternion Max-related convolution (Qmax), which uses the Hamilton transform to enhance node characterization by exploiting inter-channel connections;second, Structure Inference Block (SIF Block), which enriches node information through Bidirectional Gated Recurrent Unit (Bi-GRU)-based structure inference, fusing graph neural network node features with global features. Experimental results on three standard texture datasets demonstrate the competitive classification performance of our method. © 2025 SPIE.

关键词： Graph neural networks

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PETer: A lightweight human action recognition network based on millimeter-wave radar sparse point clouds 16

PETer: A lightweight human action recognition network based ...

引用

16th international conference on signal processing Systems, ICSPS 2024

作者： Sun, Kangsheng Jin, Biao Cai, Yan Li, Rongmin Jiangsu University of Science and Technology Ocean College Zhenjiang China Suzhou Zadar Vision Technology Co. Ltd. Suzhou China

ISBN: (纸本)9781510689251

Millimeter-wave radar, with its excellent environmental adaptability, high resolution, strong penetration capability, and privacy protection, is widely used in smart homes, elderly care, and security monitoring. However, due to the high sparsity of millimeter-wave point cloud data, it carries less information compared to video or LiDAR, posing significant challenges for precise and rapid human action recognition. This paper proposes a lightweight long-sequence spatiotemporal edge convolution Transformer network (PETer). The model first enhances the data, then separates the spatial and temporal information of the point cloud sequence, utilizes EdgeConv to capture the spatial geometric structure information of single-frame point clouds, and employs a Transformer to extract temporal features from the multi-frame spatial information. To validate the effectiveness of the model, this paper uses the IWR1443-ISK millimeter-wave radar as the detection sensor to capture human actions and generate a 3D point cloud dataset. This dataset covers 12 types of human actions from 7 volunteers, including walking, waving, boxing, and standing. The proposed model achieves an average accuracy of 98.50% on the dataset and 100% accuracy on the MARS human skeleton dataset. Compared to various point cloud networks, the PETer network is the smallest in size, fastest in speed, and highest in accuracy, demonstrating its superior performance in millimeter-wave radar human action recognition tasks. © 2025 SPIE.

关键词： Musculoskeletal system

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Less is more: Efficient Scene Graph Generation with reparameterization

Less is more: Efficient Scene Graph Generation with reparame...

引用

2025 IEEE international conference on Acoustics, Speech, and signal processing, ICASSP 2025

作者： Hong, Jonghwan Noh, Seonghyeok Ku, Bonhwa Ko, Hanseok Vision Information Processing Engineering Korea University Seoul Korea Republic of Electrical Engineering Korea University Seoul Korea Republic of

ISBN: (纸本)9798350368741

Scene Graph Generation (SGG) aims to identify objects and their relationships in visual scenes but faces two key challenges: high computational overhead, particularly for real-time applications, and the long-tailed distribution of predicates, which biases models toward frequent relationships. To address these challenges, we propose Reparams-SGG, a lightweight and efficient network architecture composed of multi-path residual blocks. This architecture reduces computational overhead by leveraging a reparameterization strategy that minimizes sequential and parallel processing, making it highly efficient during inference. Moreover, we introduce a dynamic focal loss that dynamically adjusts the temperature scale during training to focus learning on rare predicates, promoting progressively unbiased learning. Additionally, we propose a dynamic distribution loss, compensating for learning limitations solely from one-hot distributions under data imbalance conditions. We evaluate our method on the widely-used Visual Genome and the recent PSG dataset. Reparams-SGG achieves competitive performance with significantly fewer parameters than state-of-the-art models, demonstrating its efficiency and suitability for deployment in resource-constrained environments. © 2025 IEEE.

关键词： Efficient scene graph generation reparameterization Unbiased scene graph generation

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Facial Deblurring and Recognition Using Image processing and Machine Learning Techniques 8th

Facial Deblurring and Recognition Using Image Processing and...

引用

8th IFIP TC 12 international conference on Computer, Communication and signal processing, ICCCSP 2024

作者： Subrahmanyam, B.R. Janavi, T.S. Keerthana, V. Serajudeen, Ayesha Jumana Arunnagiri, A.M. Department of Electronics and Communication Engineering College of Engineering Guindy Anna University Chennai600025 India

ISBN: (纸本)9783031736162

The project addresses the challenge of accurately identifying blurred faces in computer vision and facial recognition. It introduces a novel framework that integrates deblurring techniques, utilizing point spread function deconvolution to enhance facial image quality. Principal Component Analysis (PCA) is employed for feature extraction, and a K-Nearest Neighbors (KNN) classifier is applied for face identification. The combined deblurring and PCA-transformed features improve matching and identification accuracy, particularly in scenarios with initially blurred images. Experimental validation on a real-world dataset demonstrates the efficacy of the proposed methodology. This approach not only enhances facial recognition accuracy but also lays the groundwork for future research in challenging practical applications, such as security and law enforcement. © IFIP international Federation for Information processing 2025.

关键词： Optical transfer function

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Integrating Failures in robot Skill Acquisition with Offline Action-Sequence Diffusion RL

Integrating Failures in Robot Skill Acquisition with Offline...

引用

international conference on Acoustics, Speech, and signal processing (ICASSP)

作者： Hecheng Wang Lizhe Qi Yunquan Sun Academy for Engineering & Technology Fudan University Shanghai China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Recent advancements in robot learning leverage large language models (LLMs) and sampling-based task and motion planning (TAMP) modules for automatic and scalable robot data generation. This method yields both success trajectories and a large number of failure trajectories. Prior works typically filter out failure data and adopt behavior cloning (BC) to train policy. However, this significantly reduces the sample efficiency of the method and results in a policy limited by the data collection behavior policy. In this paper, we introduce a vision-language-conditioned action-sequence diffusion policy and an action-sequence diffusion policy learning with Q-guided refinement for its training. We first redefine the reverse process of the diffusion model as the distribution of action sequences conditioned on visual observations and language instructions. We then employ BC to pretrain the policy on the success sub-dataset. Next, we optimize the action-sequence Q-value function by minimizing the temporal difference error across the complete dataset. Finally, we integrate guidance from the Q-value function into the BC loss of the reverse diffusion chain. Our method significantly outperforms baseline methods in terms of success rate and sample efficiency. By effectively leveraging failure data to optimize the policy, our method can achieve results comparable to those trained with the complete success sub-dataset while requiring 20%-30% less success data.

关键词： Training Visualization Uncertainty Large language models Data collection signal processing robot learning Trajectory Planning Speech processing

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Extracting Sparse Specialist Models from Generalist Models

Extracting Sparse Specialist Models from Generalist Models

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2025 IEEE international conference on Acoustics, Speech, and signal processing, ICASSP 2025

作者： Yu, Tao Zhao, Xu An, Yongqi Zhu, Guibo Tang, Ming Wang, Jinqiao Institute of Automation Chinese Academy of Sciences Beijing China School of Artificial Intelligence University of Chinese Academy of Sciences Beijing China Wuhan AI Research Wuhan China

ISBN: (纸本)9798350368741

Recently, several generalist models such as Contrastive Language Image Pre-training (CLIP) have demonstrated their capabilities of performing diverse downstream tasks through zero-shot or few-shot guidance. When these generalist models are used for the specific downstream task where only a fraction of features is relevant, they would suffer from a significant redundancy of parameters. While existing methods aim to achieve sparsity and specialization, they often require additional training and large datasets. In this paper, we propose a novel framework to extract a sparse specialist model from a generalist model using only few-shot samples, without any training. Our task-specific pruning framework defines task relevance metrics and employs weighted layer-wise pruning, preserving relevant features while removing redundancies. Experiments show that our method maintains nearly identical zero-shot accuracy compared to the original generalist models at 30% sparsity, with only minimal decline at 50%. © 2025 IEEE.

关键词： Network Compression Network Pruning vision-Language Models

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Predicting Biomechanical Risk Factors for Division - I Women's Basketball Athletes

Predicting Biomechanical Risk Factors for Division - I Women...

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2025 IEEE international conference on Acoustics, Speech, and signal processing, ICASSP 2025

作者： Shah, Aayushi Agarwal, Vanaja Shah, Dhairya Jani, Harman Sharma, Srishti Kaya, Tolga Taber, Christopher Raval, Mehul S. Ahmedabad University Ahmedabad India Sacred Heart University Fairfield United States

ISBN: (纸本)9798350368741

Collegiate basketball is characterized by high-impact movements such as jump landings, making athletes more susceptible to injuries. Critical biomechanical factors like knee flexion, lateral trunk flexion, and foot landing asymmetry are strongly associated with injury risk. This study aims to predict six biomechanical risk factors in the landing error scoring system (LESS). The dataset comprises 8600 video frames of counter-movement jumps (CMJs) from 17 NCAA Division I female basketball athletes, recorded from frontal and lateral perspectives and annotated using a customized error annotation algorithm. The study uses the You Only Look Once (YOLOv5nu) model to analyze the basketball athletes' CMJ videos. It demonstrates high reliability in predicting risk factors with an average Box Precision (Box P) of 0.800, recall (R) of 0.877, and mean Average Precision at IoU threshold 0.5 (mAP@0.5) of 0.879. © 2025 IEEE.

关键词： Biomechanical analysis collegiate basketball computer vision deep learning injury risk prediction sports

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