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Optimizing Receptive Field and Multi-Scale Features for Object Detection with Lightweight Hybrid Attention

Optimizing Receptive Field and Multi-Scale Features for Obje...

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Image Processing, computer Vision and Machine Learning (ICICML), international conference on

作者： Tong Yi Dawei Dai Zhanyuan Li Chongqing Key Laboratory of Computational Intelligence Chongqing University of Posts and Telecommunications Chongqing China

ISBN: (数字)9798350355413

ISBN: (纸本)9798350355420

Currently, object detection models have achieved a relatively ideal balance between speed and performance. However, taking the YOLO model as an example, it relies on Convolutional Neural Networks (CNN) to extract image features. Traditional convolutions face limitations in capturing multi-scale features, and increasing the receptive field often comes with significant computational overhead. Moreover, existing channel attention mechanisms tend to overlook the multi-scale feature space, while hybrid spatial-channel attention approaches face the challenge of high computational complexity. To address these issues, we propose a lightweight attention module that combines spatial and channel information, integrating both local and global features. Additionally, we design a novel multi-scale hybrid convolution module, which significantly enhances multi-scale feature representation and receptive field, while maintaining low computational complexity. We evaluated our method on the COCO and VisDrone datasets, and the experimental results demonstrate that the proposed model performs excellently across multiple metrics, significantly improving detection performance.

关键词： YOLO measurement Heating systems Attention mechanisms Computational modeling Feature extraction Magnetic heads Convolutional neural networks Computational complexity Faces

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Comprehensive Comparative Analysis of Food Classification Using YOLOv8n, VGG19, and InceptionV3

Comprehensive Comparative Analysis of Food Classification Us...

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Emerging Smart Computing and Informatics (ESCI), conference on

作者： Mohit Sapat Sarthak Wadegaonkar Sarthak Patankar Pratibha Mahajan Department of Computer Science Engineering Vishwakarma University Pune India

ISBN: (数字)9798331515683

ISBN: (纸本)9798331515690

Food classification plays a vital role in areas such as food recognition, nutritional tracking, and culinary exploration. This study evaluates the effectiveness of three deep learning models—YOLOv8n, VGG19, and InceptionV3—utilizing the In-dian Food Classification Dataset and Food-11. Metrics like accuracy, precision, recall, and F1 score indicate that InceptionV3 consistently delivers the highest accuracy, whereas YOLOv8n stands out for its real-time efficiency, making it well-suited for mobile or embedded systems. VGG19 provides a balanced performance but is limited by its larger model size and higher computational requirements. Statistical analysis (p ¡ 0.05) shows significant differences among the models, with confusion matrices revealing difficulties in classifying visually similar foods. The study suggests using InceptionV3 for tasks requiring precision, YOLOv8n for applications where speed is critical, and VGG19 for scenarios needing a balanced approach, while also proposing future research in cross-cultural food recognition, AI-driven food interaction, and personalized nutrition systems.

关键词： Deep learning Training measurement Analytical models Accuracy Embedded systems Statistical analysis Computational modeling Real-time systems Informatics

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LeafYogi: Deep Learning based Mobile Application Framework for Ayurvedic Leaf Classification

LeafYogi: Deep Learning based Mobile Application Framework f...

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international conference on Inventive Computation Technologies (ICICT)

作者： Chalmers J Senthil Kumar Thangavel I Johnson Department of Electronics and Communication Engineering Amrita School of Engineering Coimbatore Amrita Vishwa Vidyapeetham India Department of Computer Science and Engineering Amrita School of Computing Coimbatore Amrita Vishwa Vidyapeetham India Department of Plant Pathology Tamil Nadu Agricultural University Coimbatore India

ISBN: (数字)9798350359299

ISBN: (纸本)9798350359305

Indian forest is the principal depository for useful medicinal herbs. Medical plants along with leaves have medical values and helps in curing many diseases. But correctly identifying leaves is laborious that takes lots of time and expertise. Therefore, the identification of these medical species is a challenging task. As a result, vision based approach helps the regular people in rapid identification of herbal leaves. This paper highlights vision based system to recognise herbal leaf by creating advanced deep learning models such as YOLOV5, YOLOV8 and Resnet-50. The Dataset for Indian Medicinal Plant Species Analysis and Recognition (DIMPSAR) which was published in 2023 is used to evaluate deep learning models. The experiment is performed on Colab environment. The evaluation metrics showed that YOLOV8 outperforms the other two models in term of accuracy with 99.2 percentage. The best architecture is further deployed in mobile application using teachable machine.

关键词： Deep learning YOLO measurement Computational modeling computer architecture Robustness Real-time systems

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Using Digital Twins and Gen AI to Optimize Plastics Densification in the Recycling of Polypropylene (PP) and Polyethylene (PE)

Using Digital Twins and Gen AI to Optimize Plastics Densific...

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international conference on System modeling & Advancement in Research Trends (SMART)

作者： Rahul Vadisetty Anand Polamarasetti Electrical Engineering Wayne State University Detroit MI USA Computer Science Andhra University Visakhapatnam AP India

ISBN: (数字)9798350380583

ISBN: (纸本)9798350380590

Some critical challenges persist in the recycling of PP and PE because of some inefficiency in their densification processes, which is always burdening its quality and cost. The present research work focuses on proposing an integrated framework that applies Digital Twins and Generative AI for optimizing plastics densification. Digital Twins will digitally represent the physical recycling process and allow for monitoring in real-time, simulating various key parameters such as temperature, pressure, and size of particles. Meanwhile, the use of Gen AI will be meant to determine optimization of these values through the generation of adaptive models that can predict the conditions for maximum improvement in the densification process. Therefore, the proposed approach aims at improving the densification efficiency, reducing energy usage, and improving the quality of both recycled PP and PE. The approach was validated by extended simulations; the results obtained accordingly showed a high percentage of improvement with respect to traditional methodologies concerning process optimization and resource usage. This study provides the underground for new methodology, which will resolve the existing gaps present in current recycling technologies. Also, the Digital Twin coupled with Gen AI can be used as a revolutionary technology within the recycling industry. Further studies can broaden the scope to plastics of other types, and the use of more advanced AI models can be applied to optimize integration.

关键词： Temperature sensors Temperature measurement Adaptation models Biological system modeling Real-time systems Recycling Digital twins Plastics Optimization Monitoring

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Optimization of Graph Convolutional Networks with Variational Graph Autoencoder Architecture for 3D Face Reconstruction Task

Optimization of Graph Convolutional Networks with Variationa...

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Intelligent systems and computer Vision (ISCV)

作者： Mahfoudh M. Batarfi Manohar Mareboyana Department Of Computer Science Bowie State University Bowie MD USA

ISBN: (数字)9798350350180

ISBN: (纸本)9798350350197

The study addresses the fundamental challenges encountered in 3D face reconstruction, including the inadequacy of dedicated research initiatives, the complexity of hyperparameter optimization, the lack of accessible guidance for newcomers, and the dependency on low-quality datasets. To surmount these obstacles, the study embarks on the optimization of a Variational Graph Autoencoder (VGAE)-based architecture enriched with five distinct Graph Convolutional Network (GCN) layers, namely GCNConv, SGConv, SSGConv, GraphConv, and Chebyshev. The primary objective is twofold: first, to curate a diverse and high-fidelity 3D face dataset capable of meeting the criteria of this study posed by large-scale data intricacies, and second, to refine VGAE architectures through the strategic integration of these GCN layers. This integration aims to bolster the reconstruction capabilities of VGAE architectures and ascertain the optimal GCN layer type for maximizing accuracy (by minimizing validation loss) and computational efficiency (measured in execution time). Furthermore, the study provides comprehensive guidance tailored for researchers navigating the complexities of 3D face reconstruction. Through these concerted efforts, the findings of this research empower researchers to tackle challenging tasks such as facial analysis and reconstruction across various domains. The study endeavors to propel the field of 3D face reconstruction forward, offering valuable insights and methodologies to enhance the efficacy of VGAE architectures across diverse applications and domains. Moreover, the techniques and knowledge derived from this study can contribute to developing robust and efficient approaches for Generative AI applications beyond 3D face mesh reconstruction.

关键词： Solid modeling Three-dimensional displays Face recognition Computational modeling computer architecture Hyperparameter optimization Loss measurement

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Broccoli Classifiers: CNN and AdaBoost Models for Differentiating Indian Varieties

Broccoli Classifiers: CNN and AdaBoost Models for Differenti...

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Emerging Technology (INCET), international conference for

作者： Arshleen Kaur Rishabh Sharma Mukesh Kumar Aditya Verma Chitkara University Institute of Engineering and Technology Chitkara University Punjab India Computer Science and Engineering Graphic Era Hill University Dehradun Uttrakhand India Computer Science and Engineering Graphic Era Deemed to be University Dehradun Uttarakhand India

ISBN: (数字)9798350361155

ISBN: (纸本)9798350361162

This study looks at three main kinds of broccoli - Pusa KTS-1, Green Magic, and Calabrese. It uses a method called Convolutional Neural Networks (CNN) along with AdaBoost ensemble methods to see how these different types grow in Indian farms. They used a set of 1500 carefully taken pictures to teach and test how well the model works. A new kind of computer program called CNN and another one named AdaBoost joined together to make a very good score. They were able to tell different types apart with 97.10% accuracy by looking closely at small changes in pictures. Table I shows the model's strong accuracy, recall, and F1 scores. This confirmed that it is reliable at correctly grouping each kind while catching many different examples from every category fairly. Figure 3 showed the new method was better than the old ways. So, it is at the top for classifying different types of broccoli. Beyond just farming, this study could lead to wider uses in automated sorting systems. These changes can affect how farmers grow and sell their crops. The success of the study shows how well machine learning models can make plant variety classification more accurate and automate it. In the future, the model can be improved by adding more data and making it better. This will lead to higher accuracy when using it on farms or with new tech gadgets. This study is a big step to change how computer systems classify types of vegetables. It brings important help in farming practices and making markets different from each other.

关键词： measurement Accuracy Computational modeling Neural networks Data models Teamwork Convolutional neural networks

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On the Reduction of Large-Scale Room Acoustic Models

On the Reduction of Large-Scale Room Acoustic Models

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international conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Pavlos Stoikos Olympia Axelou George Floros Nestor Evmorfopoulos George Stamoulis Department of Electrical and Computer Engineering University of Thessaly Volos Greece

Efficient sound density simulation for room acoustic models is a challenging problem, due to the need for the solution of large-scale systems of equations that require unreasonably long computational times. However, in many cases, the measurement of sound density is not required to be computed at every point of the entire room but only at certain spots. This makes the room acoustic problem amenable to Model Order Reduction (MOR) techniques. Moment-Matching (MM) techniques are well established and can be directly applied in the resulting sound diffusion equation. In this paper, we propose a computationally efficient MM algorithm based on extended Krylov subspace method, that can generate very compact models in order to efficiently simulate them across many time-steps. Experimental results demonstrate a speedup up to 1016× with relative error less than 0.5%.

关键词： Density measurement Computational modeling Signal processing algorithms Signal processing Mathematical models Acoustics Numerical models

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Human-Variability-Respecting Optimal Control for Physical Human-Machine Interaction

Human-Variability-Respecting Optimal Control for Physical Hu...

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IEEE international Workshop on Robot and Human Communication (ROMAN)

作者： Sean Kille Paul Leibold Philipp Karg Balint Varga Sören Hohmann Institute of Control Systems (IRS) Karlsruhe Institute of Technology (KIT) Karlsruhe Germany

ISBN: (数字)9798350375022

ISBN: (纸本)9798350375039

Physical Human-Machine Interaction plays a pivotal role in facilitating collaboration across various domains. When designing appropriate model-based controllers to assist a human in the interaction, the accuracy of the human model is crucial for the resulting overall behavior of the coupled system. When looking at state-of-the-art control approaches, most methods rely on a deterministic model or no model at all of the human behavior. This poses a gap to the current neuroscientific standard regarding human movement modeling, which uses stochastic optimal control models that include signal-dependent noise processes and therefore describe the human behavior much more accurate than the deterministic counterparts. To close this gap by including these stochastic human models in the control design, we introduce a novel design methodology resulting in a Human-Variability-Respecting Optimal Control that explicitly incorporates the human noise processes and their influence on the mean and variability behavior of a physically coupled human-machine system. Our approach results in an improved overall system performance, i.e. higher accuracy and lower variability in target point reaching, while allowing to shape the joint variability, for example to preserve human natural variability patterns.

关键词： Human computer interaction Accuracy Control design Computational modeling Human-machine systems Noise Stochastic processes Process control Optimal control Behavioral sciences

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On One Method for Choosing the Planned Trajectory of Parallel Multi-Link Manipulators as Mechatronic systems with Geometric Constraints

On One Method for Choosing the Planned Trajectory of Paralle...

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international Russian Automation conference (RusAutoCon)

作者： Aleksandr Ya. Krasinskiy Andrey Yuldashev Scientific Department of Rosbiotech Dept. of Computer Modeling and Probability Theory Moscow Aviation Institute (National Research University) Moscow Russia Faculty Number 8 Information Technology and Applied Mathematics Moscow Aviation Institute (National Research University) Moscow Russia

Robotic manipulators, as the most versatile class of modern automatic devices, are part of most automated production systems. The versatility of robots is largely associated with the developed software of the controllers included in their composition. However, despite certain achievements, there are no sufficiently effective commercial software products that solve the problem of planning the trajectories of multilink manipulators. The problem of trajectory planning acquires particular complexity for organizing the joint work of several manipulators, which is relevant for engineering practice: in order to avoid collisions, it is necessary to control the trajectory not only of the working body, but also of all nodes of each manipulator. In view of the obvious ambiguity for the solution of the inverse kinematics problem, necessary for planning, it is necessary to introduce some criteria that exclude ambiguity. In this paper, we discuss the possibility for applying the methods of analytical mechanics to implement the proposed approach - the choice of a trajectory based on the minimum energy consumption along the planned trajectory.

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A Novel Classification Model Based on DWT and CNN-LSTM Motor EEG Imagination Signals

A Novel Classification Model Based on DWT and CNN-LSTM Motor...

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Pattern Recognition and Artificial Intelligence (PRAI), international conference on

作者： Yuyao Yang Zuowen Chen Weisu Li Yahong Ma School of Electronic Information and Xi'an Key Laboratory of High Pricision Industrial Intelligent Vision Measurement Technology Xijing University Xi'an China

ISBN: (数字)9798350350890

ISBN: (纸本)9798350350906

Feature extraction and pattern classification play important roles in brain-computer interface (BCI) systems, which are widely used in rehabilitation medicine, artificial intelligence and other fields. Although advanced EEG acquisition technology can generate a large amount of EEG data for different brain regions, it inevitably has disadvantages such as high cost, long time consuming and inherent high false positives. Considering the contradiction between data volume and accuracy, this paper proposes a MI EEG feature extraction and classification method based on discrete wavelet transform (DWT) and Convolutional Neural Network-Long Short Term Memory (CNN-LSTM). Based on the project BCI motor imagination EEG(MI-EGG) data set, all evaluation indexes of the proposed CNN-LSTM model are superior to other models. The prediction accuracy, precision, recall rate, F1-score and MCC of C3 and C4 channels reaches 96.43%, 96.57%, 96.57%, 96.57% and 93.02%, respectively. The reliability of the proposed model is also proved by tenfold cross-validation. The average accuracy is 99.64%, which is far higher than the other models. It can be concluded that it is a reliable method to predict MI-EEG signals by using CNN-LSTM to train the modeling ability of images.

关键词： Accuracy Computational modeling Brain modeling Motors Feature extraction Electroencephalography Data models Discrete wavelet transforms Convolutional neural networks Reliability

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