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检索条件"任意字段=Proceedings of the 3rd International Conference on Control and Computer Vision"

共 18726 条记录，以下是51-60 订阅

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Road Rage Detection System Using Deep Learning and computer vision 3

Road Rage Detection System Using Deep Learning and Computer ...

引用

3rd international conference on Integrated Circuits and Communication Systems, ICICACS 2025

作者： Lohare, Shaik Tanveer Maaz, Mohammed Razi, Mahammad Nehal, Mohammed Ahmed, Syed Thouheed Rashmi, C. School of Computing and Information Technology Reva University Bengaluru India

ISBN: (纸本)9798331508456

Road rage detection in India's chaotic traffic conditions demands robust, real-time solutions. This paper proposes a 3D CNN-based system that leverages transfer learning from a violence detection model (98 % accuracy on Kaggle datasets) to a road rage-specific model (94 % accuracy on a custom Indian dataset). By incorporating temporal smoothing with a 5-frame sliding window, the system reduces false positives by 15 % compared to existing methods. Experiments demonstrate its efficacy under varying illumination and traffic densities, validated through precision (0.99 for non-violence) and recall (0.99 for violence). The system's deployment in Bangalore highlights its potential for traffic monitoring, law enforcement, and insurance claim verification. Future work includes edge-device optimization and multimodal data integration. © 2025 IEEE.

关键词： Traffic control

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Artificial Intelligence, Intelligent Manufacturing, and Sustainable Futures in Next-Gen Textiles: A Comprehensive Approach 3

Artificial Intelligence, Intelligent Manufacturing, and Sust...

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3rd international conference on Intelligent Data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Shabi, Sathika Begum Mohammad Bhuvaneshwari, M. Kanimozhi, S. Pavithra, S. Department of Computer Science and Engineering Chennai Institute of Technology Tamilnadu Chennai600069 India

ISBN: (纸本)9798331527549

The next revolutionary innovation in the textile industry is artificially driven by intelligent quality control and next-generation textile materials. This review, based on AI-driven automation and sustainable practices in textiles, provides a comprehensive overview of how emerging technologies are reshaping manufacturing processes, quality control, and functionalities in textiles. It examines the use of machine learning in defect detection, the integration of smart materials, and the adoption of recycled and bioengineered fabrics, thus helping producers to overcome issues associated with fabric production, environmental impact, quality control, and scalability. The synthesis of recent advances enables this study to critically review literature and case studies on AI, smart materials, and sustainable practices in textiles. It lays a pathway for future developments and advancements in the textile industry. © 2025 IEEE.

关键词： Smart manufacturing

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Research on brain signal acquisition and transmission via non-invasive brain-computer interface 3

Research on brain signal acquisition and transmission via no...

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3rd international conference on Algorithms, Network, and Communication Technology, ICANCT 2024

作者： Luo, Weihao Xiong, Shiyi Li, Yan Jiang, Pan School of Information Engineering Wuhan Business University Hubei Wuhan430000 China

ISBN: (数字)9781510688902

ISBN: (纸本)9781510688896

Non-invasive brain-computer interface technology has become a research hotspot in the field of human-computer interaction with its characteristics of no surgery and safe reliability. This paper introduces the non-invasive brain signal acquisition and transmission of brain-computer interface technology, which aims to enable people with disabilities to conduct peripheral control through thinking. Brain wave signals were collected by using electroencephalography (EEG) technology and functional magnetic resonance imaging (fMRI) technology. This paper focuses on the use of brain signal analysis, correlation function analysis and independent component analysis (ICA) to collect the signal for comprehensive noise processing and artifact processing, and finally the processed signal instructions are transmitted to the corresponding peripherals to control it. The device not only has the function of signal collection and transmission, but also can be customized according to the needs of users to complete the function of fitting the user, which can effectively help the disabled to live independently and improve the quality of life. © 2025 SPIE.

关键词： Brain computer interface

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Few-Shot Image Classification Method Based on Self-Supervised Task Pre-training 3

Few-Shot Image Classification Method Based on Self-Supervise...

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3rd international conference on Artificial Intelligence and Education, ICAIE 2024

作者： You, Liang Lin, Youping Cheng Yi College Jimei University Fujian Xiamen China

ISBN: (纸本)9798400712692

Deep learning has received a lot of attention in recent years and has rapidly made significant progress in research and practical applications, and has become a major approach in computer vision. However, it relies on a large annotated dataset, and the process of collecting and labeling these datasets is both labor-intensive and expensive, often making it difficult to obtain large datasets. Therefore, it is particularly important to enable deep learning to adapt to small sample size situations. Research on learning with fewer samples is still in its infancy, and the performance gap is obvious when compared to large sample scenarios. In this paper, we address this problem by exploring image classification and target detection under sample less conditions. We propose a few-shot image classification method using self-supervised pre-training to enhance the generalization capability. Experimental results show that the method exhibits competitive performance. © 2024 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： computer vision deep learning few-shot learning image classification object detection

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Intelligent Traffic Management System using Multi-Agent Reinforcement Learning 3

Intelligent Traffic Management System using Multi-Agent Rein...

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3rd international conference on Electronics and Renewable Systems, ICEARS 2025

作者： Shaheen, Dalaali Paulraj, Getzi Jeba Leelipushpam Jebadurai, Immanuel Johnraja Kirubakaran, Stewart Karunya Institute of Technology and Sciences Division of Computer Science and Engineering Coimbatore India Karunya Institute of Technology and Sciences Division of Cse Coimbatore India

ISBN: (纸本)9798331509675

This paper presents a decentralized multi-agent system for intelligent traffic management in urban environments, where each agent represents a traffic light controller at an intersection. The proposed system leverages a combination of Distributed W-Learning and Deep Q-Networks (DQN) to optimize traffic flow. Distributed W-Learning enables agents to prioritize decisions based on multiple performance policies, while DQN enhances their ability to handle complex state-action mappings through neural network approximations. By utilizing locally available traffic data, each agent adapts dynamically to diverse traffic conditions. The integration of these machine learning techniques ensures a fully decentralized and self-organizing approach, minimizing congestion, improving traffic efficiency, and addressing multiple objectives simultaneously. Simulation experiments are conducted using SUMO (Simulation of Urban MObility) to evaluate the system's performance in realistic traffic scenarios. This study highlights the potential of combining Distributed W-Learning and DQN in multi-agent reinforcement learning to revolutionize traffic management systems and achieve scalable, adaptive, and efficient urban traffic control. © 2025 IEEE.

关键词： Traffic congestion

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Enhancing Occluded Face Recognition Through Device-Edge Collaboration and Cross-Domain Feature Fusion with Heatmaps 3

Enhancing Occluded Face Recognition Through Device-Edge Coll...

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3rd international conference on Intelligent Data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Prasanth, Ashwin Annie Micheal, A. Department of Computer Science & Engineering Sathyabama Institute of Science & Technology Chennai India

ISBN: (纸本)9798331527549

Face recognition under occlusion presents a persistent challenge in computer vision, primarily due to difficulties in capturing and effectively integrating visible and obscured facial features. This paper introduces a novel device-edge collaborative framework that advances occluded face recognition through an innovative cross-domain feature fusion technique. Our method employs a software-generated, occlusion-aware heatmap created from standard visible-light images, which emphasizes key facial landmarks even when regions of the face are partially covered. This heatmap provides a complementary data layer that enhances traditional visible facial features, allowing the cross-domain feature fusion process to synthesize a more comprehensive set of features for reliable recognition under occlusion. The combined feature set leverages both visible and simulated heatmap information to improve model robustness, even in complex occlusion scenarios. Additionally, a delay-optimized task scheduling method is applied on the edge, ensuring efficient resource allocation, faster processing, and minimal latency. Although this project is ongoing, preliminary evaluations indicate the potential effectiveness of this approach, with expected outcomes to include high recognition accuracy and reduced latency. This framework provides a scalable solution for occluded face recognition by integrating complementary data sources without requiring specialized hardware. © 2025 IEEE.

关键词： Face recognition

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Real-Time Detection of Rice Bacterial Blight using Remote Sensing and Deep Learning 3

Real-Time Detection of Rice Bacterial Blight using Remote Se...

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3rd international conference on Electronics and Renewable Systems, ICEARS 2025

作者： Rakesh, Nitin Thirumurugan, R. Mohankumar, N. Kasinathan, Prabakaran Prasad, S. N. Sheela Evangelin Srinivasan, C. Maharashtra Nagpur India Adhi College of Engineering and Technology Department of Electronics and Communication Engineering Tamil Nadu Kanchipuram India Symbiosis Institute of Technology Nagpur Campus Pune India Vel Tech Rangarajan Dr.Sagunthala R&d Institute of Science and Technology Department of Computer Science and Engineering Tamil Nadu Chennai India Sri Krishna Engineering College Department of Computer Science and Engineering Tamil Nadu Chennai India Saveetha Institute of Medical and Technical Sciences Saveetha University Saveetha School of Engineering Department of Computer Science and Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798331509675

This research introduces an innovative method for the real-time identification of bacterial blight in rice crops via remote sensing and deep learning (DL) technologies. Bacterial blight represents a significant threat to rice production, requiring quick detection for efficient control. To address this challenge, remote sensing data from drones and satellites is analyzed to detect visual signs connected to the diseases. A convolutional neural network (CNN) model is used to classify and identify sick rice plants using high-resolution images. The results demonstrate that the model attained an accurate rate of 98.2%, significantly enhancing the speed and precision of illness identification relative to conventional approaches. The combination of advanced DL algorithms with remote sensing technologies enables quick responses and improves sustainable agriculture practices. It illustrates the capability of using technology for crop monitoring, enhancing food security, and optimizing resource management in agriculture. © 2025 IEEE.

关键词： Convolutional neural networks

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Diabetic Foot Ulcer Unsupervised Segmentation with vision Transformers Attention 4th

Diabetic Foot Ulcer Unsupervised Segmentation with Vision T...

引用

4th Diabetic Foot Ulcers Grand Challenge, DFUC 2024 held in Conjunction 27th international conference on Medical Image Computing and computer Assisted Intervention, MICCAI 2024

作者： Brodzicki, Andrzej Kucharski, Dariusz Moniak, Bartlomiej Kostuch, Aleksander Noworolnik, Filip Anna, W. Jaworek-Korjakowska, Joanna Department of Automatic Control and Robotics AGH University of Kraków Kraków Poland Center of Excellence in Artificial Intelligence AGH University of Kraków Kraków Poland

ISBN: (纸本)9783031808708

The Diabetic Foot Ulcer Challenge is a global computer vision competition organised to raise awareness of the complications caused by diabetes and motivate researchers to develop solutions that might help in the treatment. In 2024, the challenge focused on self-supervised learning methods and synthetic data, as the process of collecting and labelling medical data is a tedious one. In this research, we present a novel, two-stage method for self-supervised DFU segmentation. It is based on a vision Transformer attention from classification pre-task, followed by U-Net weakly supervised segmentation refinement. Our solution took 3rd place on the challenge leaderboard, with a 0.4796 Dice Score on the test set. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Distribution transformers

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A Review on the Use of Smart Agriculture Systems to Protect Crops 3

A Review on the Use of Smart Agriculture Systems to Protect ...

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3rd international conference on Electronics and Renewable Systems, ICEARS 2025

作者： Kumar, Gulshan Gourshettiwar, Palash Suman, Saurabh Kumar, Shravan Kumar, Dhiraj Raushan, Rahul Faculty of Engineering and Technology Department of Computer Science and Design Maharashtra Wardha India Faculty of Engineering and Technology Department of Computer Science and Medical Engineering Maharashtra Wardha India

ISBN: (纸本)9798331509675

Smart agriculture systems leverage the possibilities offered by cutting-edge technologies such as IoT, AI, and remote sensing to revolutionize conventional farming by enhancing resource utilization, production, and crop damage mitigation. Real-time monitoring of soil and crop health, predictive analytics, pest control, and precision irrigation measures are all enabled by these systems. They are able to address major Indian agriculture issues, consequently boosting yield and profitability and promoting environmental sustainability. The largescale deployment of intelligent agriculture systems will change the agriculture landscape in India and will assure long-term food security for an ever-growing population. Challenges include adequate research and future studies in order to better install and achieve smart agricultural systems to protect crops. Intelligent agriculture involves all advanced research, including science and innovations, in national development through space technologies to enhance soil quality, conserve water, and facilitate agriculture information. Space ventures will undergo improved modernization through the introduction of crop sprayers, precision gene editors, epigenetics, big data analytics, IoT, wind and photovoltaic smart energy, AI-enabled robotic applications, and wide-scale desalination technologies. Implementing digital farming systems in developing economies will help their sectors as 85 percent of the global population is set to live in developing countries by 2030. Automation will prove to be necessary since food scarcity is on the rise along with resource wastage. control strategies such as the IoT, aerial imagery, machine learning, and artificial intelligence will boost production and prevent soil degradation. These advanced technologies are also able to alleviate such issues as plant disease detection, pesticide management, and water application. The introduction of the Internet of Things in the agricultural research world has started

关键词： Smart agriculture

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Smart Agriculture: Enhancing Crop and Weed Detection Using MobileNetV2 for Autonomous Farming Systems 3

Smart Agriculture: Enhancing Crop and Weed Detection Using M...

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3rd international conference on Intelligent Systems, Advanced Computing, and Communication, ISACC 2025

作者： Uma, P. Pranav, S. Ma, Sutharson Vignesh, V. Nandha Engineering College Department of Computer Science and Engineering Erode India

ISBN: (纸本)9798331523893

Agriculture is vital to food security and economic stability, but weeds reduce crop yields by competing for sunlight, water, and nutrients. Manual weeding and herbicides are often ineffective and environmentally harmful. This study presents an autonomous weeding robot that utilizes MobileNetV2 for real-time crop and weed detection, leveraging its lightweight architecture for efficient performance on devices like the NVIDIA Jetson Nano. The system achieves 85% accuracy, a 0.72 IoU score, and 25 FPS processing speed, with field tests showing detection accuracy of up to 90% in ideal conditions. By reducing herbicide use and improving resource efficiency, this approach supports sustainable agriculture, with future advancements such as multispectral imaging and pest detection further enhancing its role in precision agriculture. © 2025 IEEE.

关键词： Autonomous Weeding computer vision Crop Detection MobileNetV2 Smart Agriculture Weed Detection

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