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Transfer Learning with EfficientNet for computer vision based Automatic Gender Classification 3

Transfer Learning with EfficientNet for Computer Vision base...

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3rd international conference on Advances in Electrical, Computing, Communication and Sustainable Technologies, ICAECT 2023

作者： Patel, Sanskruti Patel, Dharmendra Changa India

ISBN: (纸本)9781665494007

The past several years have seen a lot of activity in the field of gender classification research. For a wide range of tasks including surveillance, computer vision, human-computer interaction, and traffic monitoring, gender classification is essential. The traditional approach used to classify gender has a number of drawbacks and is insufficient when used with live images. However, gender classification is just one of several computer vision applications where deep learning algorithms have demonstrated their potential. CNNs are capable of automatic feature generation and provide data insight which is used for efficient classification. In this paper, a special class of pre-trained CNN, called EfficientNet is proposed for automatic gender classification from input image. The experiment is carried out using a dataset with 58,700 images categorized into male and female. The performance of EfficientNet-B0 is also compared with other benchmark pre-trained CNN models i.e. VGG16, ResNet50 and DenseNet. The Precision, Recall and F1-Score values achieved for EfficientNet-BO are 96.80%, 96.60% and 96.88% respectively. © 2023 IEEE.

关键词： computer vision

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Machine Learning-Powered Real-Time Motion Detection System: A Review 3

Machine Learning-Powered Real-Time Motion Detection System: ...

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3rd international conference on Innovative Mechanisms for Industry applications, ICIMIA 2023

作者： Khurana, Naman Bansal, Madhavi Thakur, Ananya Varshitha, Velma Sai Tannu Kaur, Er. Kirat Chandigarh University Computer Science & Engineering Punjab India

ISBN: (纸本)9798350343632

Motion detection, a fundamental technology in computer vision, is crucial for a wide array of applications, including security,surveillance, gaming and robotics. OpenCV, the open-source computer vision Library, has emerged as a versatile tool for developing robust and efficient motion detection systems. The article offers insights into the intricacies of motion detection using OpenCV, combining existing research findings with a unique perspective on this critical subject. It explores traditional and advanced techniques, highlights real-world applications and addresses persistent challenges in the field such as handling background clutter, adapting to changing lighting conditions, and achieving real-time processing. It presents a fundamental approach to motion detection, encompassing frame acquisition, frame differencing, thresholding, noise reduction, object tracking, and more, with a focus on the tools and technologies that can enhance these processes. The primary problem addressed in the article is the development of accurate, reliable, and real-time motion detection systems considering various challenges and objectives across different application domains. The provided references encompass a wide range of research in the field, contributing to a comprehensive understanding of motion detection. The article can serve as a valuable resource for individuals interested in motion detection and its integration with OpenCV, offering a foundation for readers to embark on their motion detection projects and foster innovation in this dynamic field. © 2023 IEEE.

关键词： computer vision Frame Differencing Motion Detection Object Tracking Thresholding

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FYCFNet: Vehicle and Pedestrian Detection Network based on Multi-model Fusion 3

FYCFNet: Vehicle and Pedestrian Detection Network based on M...

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3rd international conference on computer vision, Image and Deep Learning and international conference on computer Engineering and applications, CVIDL and ICCEA 2022

作者： Dai, Pnegyu Brunel University Chongqing University of Post and Telecommunication International College Chongqing China

ISBN: (纸本)9781665459112

vision-based solutions for target detection in autonomous driving are very much about the accuracy of detection. A correct or incorrect detection may cause or avoid a traffic accident. Therefore, in this paper, to further improve the detection accuracy of vision schemes, we propose a multi-model fusion network: Fusion Network with YoloV5 and CBNEet Faster-RCNN (FYCFNet) that fuses a one-stage target detection model and a two-stage model, which consists of three parts: the first part is a single-stage YOLOV5 [1] detection model, the second part is a Faster-RCNN [2] with CBNet-V2 [3] as the backbone, and the third part is the post-fusion head of weighted boxes fusion. We tested the performance of this network and compared it with other mainstream networks, and verified that the network achieves a very impressive accuracy improvement. © 2022 IEEE.

关键词： computer vision

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A study of a federated learning framework based on the interstellar file system and blockchain: Private Blockchain Federated Learning 3

A study of a federated learning framework based on the inter...

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3rd international conference on computer vision, Image and Deep Learning and international conference on computer Engineering and applications, CVIDL and ICCEA 2022

作者： Zhang, Peng Liu, Gang Chen, Zhiyu Guo, Jianwei Liu, Peng University of Technology School of Computer Science and Engineering Changchun ChangChun China Jilin Heshun Hengtong Technology Co. Product Department ChangChun China

ISBN: (纸本)9781665459112

Federated learning is a scalable machine learning paradigm in which a large group of individuals collaborates to build a high-quality machine learning model. This paper proposes Private Blockchain Federated Learning (PriChainFL). It is a federated learning method that combines blockchain and IPFS, in response to the fact that federated learning is still vulnerable to attacks such as data poisoning and member inference attacks. It also reduces the storage cost of blockchain. © 2022 IEEE.

关键词： Blockchain

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Traffic sign recognition algorithm based on convolutional neural network 3

Traffic sign recognition algorithm based on convolutional ne...

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3rd international conference on computer vision, Image and Deep Learning and international conference on computer Engineering and applications, CVIDL and ICCEA 2022

作者： Zhang, Xiaoxue Huang, Wei Wuhan Institute of Technology School of Computer Science and Engineering Wuhan China

ISBN: (纸本)9781665459112

In order to improve the recognition accuracy and recognition speed of traffic signs, a recognition model based on convolutional neural network with high accuracy and fast speed is designed for traffic sign recognition. The model uses improved Inception module and multi-scale feature fusion to enhance the feature extraction ability of the network, bath normalization to accelerate the training of the network, and global average pooling to reduce the number of parameters of the model. Training and test are conducted on the GTSRB dataset, and the recognition model achieves an accuracy of 99.6%, and the time to recognize each image is 0.22ms. © 2022 IEEE.

关键词： Convolutional neural networks

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YOLOv8: A Novel Vehicle Mirror Detection Algorithm with Enhanced Performance using Deep Learning 3

YOLOv8: A Novel Vehicle Mirror Detection Algorithm with Enha...

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

作者： Narayanan, Sundarakrishnan Donthi, Yashas Mohana Ramakanth Kumar, P. Sreelakshmi, K. Rv College of Engineering® Center for Computer Vision Research Bengaluru India Rv College of Engineering® Center for Computer Vision Research Bengaluru India

ISBN: (纸本)9798331509675

In recent times, Artificial intelligence (AI) and machine learning (ML) technologies have demonstrated remarkable capabilities in addressing real time needs and challenges across multiple domains and fields. In the world of deep learning, various Neural Network architectures like DNN, CNN, and RNN have been instrumental in advancing solutions to complex problems spanning multiple industries, business environments and law enforcement use cases. This paper implemented a special type of CNN, YOLOv8(You Only Look Once), a SOTA architecture to detect the presence of rear-view mirrors in two-wheelers for traffic surveillance and law enforcement applications. The proposed research introduces a novel idea in the realm of road safety by presenting the first ever paper on vehicle mirror detection. This approach sheds light on an often-overlooked aspect, contributing significantly to the enhancement of road safety measures. A custom dataset of 1246 training images, 155 test images and 157 validation images has been considered. This dataset has been used to train and test a binary classification model with the label "mirror". Images with the label "mirror"indicate the presence of one or both rear-view mirrors on two-wheelers. On the other hand, images with no mirrors are treated as "background"images. The model achieved a maximum precision of 93.8%, recall of 87.9%, mAP50 of 94.1% and F1-score of 90.9%. These results stand as evidence of the robustness and proficiency of the model, ensuring that it can be potentially used in real world use cases, specifically for law enforcement applications. © 2025 IEEE.

关键词： Convolutional neural networks

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Personalised AI Based Yoga-Pose Recommendation System Using Game theory 3

Personalised AI Based Yoga-Pose Recommendation System Using ...

引用

3rd international conference on Advanced Computing Technologies and applications, ICACTA 2023

作者： Parab, Nidhi Pravin Rampurawala, Alefiya Abbas Khade, Siddhi Sunil Mahato, Manimala Shah and Anchor Kutchhi Engineering College Department of Computer Engineering Mumbai India

ISBN: (纸本)9798350348347

Yoga is an ancient practice that seeks to unite the mind with divine awareness. However, to derive maximum benefits from yoga, it is important to select the appropriate asana according to an individual's needs. Not everybody has access to good yoga instructors who can make tailored yoga routines for them. An artificial intelligence (AI)-based model which uses Game theory on yoga asanas with similar benefits, could help recommend a personalized yoga routine to support users on their journey towards a healthier lifestyle. In certain cases, the person is unable to determine the part of their body that is stiff or causing unease, for example, the stiffness may be very mild or only occur during certain movements or activities. In some cases, the person may simply not be paying close attention to their body or may have become accustomed to the sensation of stiffness in that particular area. A model which uses computer vision can help overcome this issue. The traditional approach to yoga instruction relies on a one-size-fits-all approach, but Game theory offers a more nuanced approach that considers individual preferences, like medical conditions, areas of discomfort, level of experience and lifestyle goals. The model is tested using data analysis of a test dataset. The paper concludes with a discussion of the implications of the findings for the field of yoga instruction and the results suggest that Game theory is an effective tool for developing personalized yoga recommendations. © 2023 IEEE.

关键词： Python

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Discerning Art Works through Active Machine Learning 3

Discerning Art Works through Active Machine Learning

引用

3rd international conference on computer vision, Image and Deep Learning and international conference on computer Engineering and applications, CVIDL and ICCEA 2022

作者： Yu, Zihao Grinnell College GrinnellIA50112 United States

ISBN: (纸本)9781665459112

Scene classification is a popular and important question in computer vision and has been developed in different areas. Applying computer vision to artworks has become a popular topic in recent years. However, the traditional random sampling to identify the artworks through machine learning requires a large data set and, therefore, a higher cost to get a solid result. This paper compares random sampling and active learning (uncertainty sampling) performance using a data set (8446 paintings) of the 50 most influential painters in Europe from the 13th to the 20th century. and then propose that the active learning strategy can build a stronger model that requires smaller data sets. The active learning model can be further improved through training in larger data sets and applied in the artwork recognition for artificial intelligence.. © 2022 IEEE.

关键词： computer vision

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Performance Analysis of LiDAR Data Processing on Multi-Core CPU and GPU Architectures 3

Performance Analysis of LiDAR Data Processing on Multi-Core ...

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3rd international conference on Computing and Machine Intelligence (ICMI)

作者： Alzyout, Mohammad S. Al Nounou, Abd Alrahman Tikkisetty, Yashwanth Naidu Alawneh, Shadi Oakland Univ Elect & Comp Engn Dept Rochester MI 48063 USA

ISBN: (纸本)9798350372977;9798350372984

The projection process of the LiDAR 3D Point Cloud data is one of the crucial steps in computer vision applications. It involves several steps to achieve the finalized accurate results. Many current studies leverage the benefits of using a GPU in the computing capability. This paper presents a comparative study of the implementation and testing of this process on single-core, multi-core CPU and GPU architectures. The computational efficiency of each platform is evaluated through a series of benchmarks, including data extraction, segmentation, and transformation tasks. Our analysis reveals the inherent parallelization benefits of GPUs in handling large-scale point cloud data, while also considering the accessibility of multi-core CPUs. Also, a comparison between the NVIDIA RTX 3070 and NVIDIA RTX 4060 is provided. The RTX 3070 showed roughly a speed up of 8 times over the RTX 4060. In addition, the Multi-core implementation outperforms up to 10 times over the single-core. These results overall show the benefits of using the Multi-core and the GPU accelerating approaches to this application, with the availability for further improvements.

关键词： LiDAR 3D Point Cloud CPU GPU Multi-Core OpenACC CUDA ROI Extraction Segmentation Projection

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A Review on Meta-Learning: How Artificial Intelligence and Machine Learning Can Learn to Adapt Quickly 3

A Review on Meta-Learning: How Artificial Intelligence and M...

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

作者： Khadse, Shrikant Gourshettiwar, Palash Pawar, Adesh Faculty of Engineering and Technology Wardha442001 India Faculty of Engineering and Technology Department of Computer Science and Medical Engineering Wardha442001 India Department of Computer Science and Medical Engineering Maharashtra Wardha442001 India

ISBN: (纸本)9798331509675

Meta-learning aims to create Artificial Intelligence (AI) systems that can adapt to new tasks and improve their performance over time without extensive retraining. The advent of meta-learning paradigms has fundamentally changed how Artificial Intelligence (AI) and Machine Learning (ML) are handled, constantly reshaping these fields. Research study reviews the broad field of meta-learning, highlighting its critical function in bridging the AI-ML divide to improve adaptability. The study starts with a review of past developments, following the development of meta-learning algorithms and their underpinning theories. The historical view lays the groundwork for understanding the course of this dynamic regulation, spanning from early efforts in probabilistic program induction to modern discoveries in model-agnostic meta-learning. The study explores multiple meta-learning applications in AI and ML, revealing how they affect financial forecasting, computer vision, natural language processing, autonomous cars, health informatics, robotics, and personalized recommendation systems. When seen from the perspective of adaptive intelligence, meta-learning may be effectively applied to conditions with limited data, hyperparameter optimization, few-shot learning, and quick adaption in reinforcement learning settings. © 2025 IEEE.

关键词： Contrastive Learning

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