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Deep Learning Based image Classification Using Small VGG Net Architecture 2

Deep Learning Based Image Classification Using Small VGG Net...

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2nd IEEE Mysore Sub Section international conference, MysuruCon 2022

作者： Paulchamy, B. Maharajan, K. Srikanteswara, Ramya Hindusthan Institute of Technology Tamilnadu Coimbatore India Kalasalingam Academy of Research and Education School of Computing Department of Cse Krishnankoil India Nitte Meenakshi Institute of Technology Department of Cse Bengaluru India

ISBN: (数字)9781665497909

ISBN: (纸本)9781665497909

One of the most interesting & important areas of computer vision is classification. Deep learning is important in image classification because it allows for the grouping of similar images. Certain adjustments to the images must be made before feeding them to the training model, such as normalization, image resizing, grey-scaling, and so on. Following image pre-processing, the next task is to extract features from the images, draw boxes around the objects in the image, and finally classify the images based on the detection of objects in the images. In a nutshell, classification is a simple task for humans but a difficult task for machines. In this case, we'll use the Small VGGNet (VGG- Visual Geometry Group) Architecture, which is a type of CNN, to classify images using deep learning. The qualified model is validated using three random images from Google. We experimented with scaling the image to different sizes. The deep learning model, which uses Small VGGNet architecture to focus on image classification, performed exceptionally well, correctly categorizing the images. Google Photos has already implemented image classification in their app, but uploading 1000s of images to the cloud requires a strong internet connection because computation is done on Google's server. The issue here is that if we are in a remote location, accessing the internet becomes difficult. Here, we will deploy a trained model with the application to make it easier to classify animals © 2022 IEEE.

关键词： computer vision

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VDD4TS: A Vehicle Detection Dataset for Traffic Survey of Road 2

VDD4TS: A Vehicle Detection Dataset for Traffic Survey of Ro...

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2nd international conference on image processing, computer vision and Machine Learning, ICICML 2023

作者： Lu, Xu Wang, Linfei Qi, Chongchong Zhang, Sen Zhao, Zhengpeng Yunnan University School of Information Science and Engineering Yunnan Key Laboratory of Media Convergence Kunming China Yunnan United Vision Technology Company Limited Technology Center Kunming China Yunnan Provincial Highway Science and Technology Research Institute Planning Office Kunming China Yunnan University School of Information Science and Engineering Kunming China

ISBN: (纸本)9798350331417

While object detection has achieved remarkable success in tasks such as autonomous driving and intelligent transportation, there is still a gap in meeting the fine-grained detection requirements for specialized road traffic survey tasks. To address this, we built a dedicated vehicle detection dataset tailored for traffic surveys of roads, named the VDD4TS. Within monitoring scenarios, VDD4TS aims to perform detection research on ten vehicle categories, including sedans, small trucks, buses, medium-sized trucks, large trucks, extra-large vehicles, container trucks, tractors, motorcycles, and electric cars. It comprises 12,535 high-quality images, encompassing 59,242 bounding boxes. It accounts for diverse lighting and weather conditions. Furthermore, the dataset possesses three noteworthy attributes: more detailed vehicle classification, a specific focus on suburban and rural scenes that road network detection systems relatively less represent, and distinct and informative annotation details. Extensive baseline experiments were conducted in object detection tasks to validate the dataset's effectiveness. The experimental results indicate that the VDD4TS dataset poses challenges for current mainstream object detection algorithms and is suitable for evaluating more complex advanced algorithmic models. © 2023 IEEE.

关键词： dataset object detection rural roads traffic survey

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vision based Cassava Plant Leaf Disease Classification using Machine Learning Techniques 2

Vision based Cassava Plant Leaf Disease Classification using...

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2nd international conference on Automation, Computing and Renewable Systems, ICACRS 2023

作者： Aruna, R. Prabu, M. Ananthi, S. Bharathi, V.C. Sathya, R. Suchithra, B. Department of IT Kongunadu College of Engineering and Technology Tamil Nadu Trichy India Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Chennai India Department of CSE Sri Eshwar College of Engineering Coimbatore India School of Computer Science and Engineering VIT-AP University Andhra Pradesh Amaravati India Department of CSE Sri Eshwar College of Engineering India

ISBN: (纸本)9798350340235

Plant leaf infection recognition using supervised machine learning has emerged as a promising solution to address the pressing challenges in agriculture and plant pathology. Firstly, a wide-ranging dataset of real-time cassava images, comprising both infected and healthy plant leaves, is methodically amassed and accurately annotated. The real time dataset covered healthy cassava leaves, Cassava Bacterial Blight diseases, Cassava Brown Streak Disease diseases, Cassava Green Mottle diseases, Cassava Green Mottle diseases and Cassava Mosaic Disease. The data preprocessing phase involves an assessment of filtering, noise reduction, resizing, and extraction techniques to maintain uniformity and augment the dataset's variability. Following this, relevant details are extracted from the preprocessed cassava frames, which are then utilized as inputs for the machine learning model. The approach of combining Leaf Intensity Vector, Principle Component Analysis, Gray Level Co-occurrence Matrix, and Support Vector Machine is adopted for the recognition of cassava leaf diseases. Using the polynomial and RBF kernel of SVM, KNN, Random Forests, and Decision Trees, the extracted suggested features are finally categorized. The proposed cassava leaf diseases classification system performs well, providing greater SVM RBF accuracy (98.4%) on models with disordered cassava leaves. © 2023 IEEE.

关键词： image processing k-Nearest Neighbor (KNN) Leaf diseaseand Performance Measure Principal Component Analysis Random Forests (RF) and Decision Trees (DTs) Support Vector Machine (SVM)

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Object Detection Based on image Pyramid Feature Fusion and Shared Detection Head 2

Object Detection Based on Image Pyramid Feature Fusion and S...

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2nd international conference on Electronic Information Technology, EIT 2023

作者： Liu, Xiao-Sa Chen, Si-Bao IMIS Lab of Anhui Province Anhui Provincial Key Lab of Multimodal Cognitive Computation Key Lab of IC&SP of MOE School of Computer Science and Technology Anhui University Anhui Hefei230601 China

ISBN: (数字)9781510666627

ISBN: (纸本)9781510666610

In the object detection, the processing of feature fusion and the structure of detection head have an important impact on the detection performance. The current detectors often use the detection pipeline of 'backbone-feature fusion network-head'. We first propose the Feature Fusion (FF), which constructs a lightweight branching network based on the image pyramid and fuses its extracted features with those of the backbone network, providing a new idea for the focus of feature fusion. In addition, we design the Shared Detector Head (SDH). The main purpose of SDH is to reduce the inconsistency of predictions on feature maps between classification and regression tasks, enhance the interaction between the two, and enhance the detection performance. Our experiments on MS COCO2017 and PASCAL VOC0712 datasets support the above analysis. Based on the above improvements, our approach achieves 0.8% mAP improvements on MS COCO2017. The above experiments prove the effectiveness of our approach. © 2023 SPIE.

关键词： Object detection

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An Efficient and Intelligent IoT-Based Security Model for Enhanced Protection Using Motion Detection and Cloud Storage Optimization 2

An Efficient and Intelligent IoT-Based Security Model for En...

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2nd IEEE international conference on Networks, Multimedia and Information Technology, NMITCON 2024

作者： Gangadharaswamy, H.B. Kumar, Narendra Sharmila, N. Shilpa, M. Mallikarjunaswamy, S. Shivaji, R. Basavaraju, N.M. Balachandra, P. Komala, M. Jss Academy of Technical Eduction Dept of Computer Science and Engineering Bengaluru India Rns Institute of Technology Artificial Intelligence and Machine Learning Bengaluru India Jss Science and Technology University Dept of Electronics and Electrical Engineering Mysuru India Bengaluru Institute of Technology Dept of Information Science and Engineering Bengaluru India Jss Academy of Technical Eduction Dept of Electronics and Communication Engineering Bengaluru India Jagadambha college of Engineering & Technology Dept Electronics and Telecommunication Engineering India Sjb Institute of Technology Dept of Mathematics Bengaluru India Sjb Institute of Technology Dept of Electronics and Communication Engineering Bengaluru India

ISBN: (纸本)9798350372892

The proposed IoT-based Smart Security Solution (IoT-SSS) for CCTV and network nodes addresses the challenges of high data volume management, power consumption, and cost inefficiency in traditional security systems. Leveraging Python's image processing for motion detection via passive infrared (PIR) sensors, IoT-SSS intelligently stamps video recordings and sensor data, activating only when necessary. This method ensures data is initially saved on local storage, significantly reducing the need for continuous recording and, thereby, minimizing power usage and storage demands. When data hits a pre-defined threshold, influenced by location sensitivity, it's automatically uploaded to the cloud, further optimizing storage needs and eliminating manual intervention. This smart approach not only conserves energy by powering down inactive devices but also slashes maintenance costs. Time, date, and sensor information are embedded directly onto each video frame, facilitating easy review without excessive data overload. Simulation results demonstrate that IoT-SSS outperforms traditional security systems in terms of data storage efficiency and power consumption, offering a cost-effective and energy-efficient alternative for modern security needs. © 2024 IEEE.

关键词： Cloud upload Data storage efficiency IoT-based Smart Security Solution (IoT-SSS) Motion detection Power consumption

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DouGNN: An End-to-End Deep Learning Framework for Predicting Individual Behaviors from fMRI Data 2

DouGNN: An End-to-End Deep Learning Framework for Predicting...

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2nd international conference on image processing, computer vision and Machine Learning, ICICML 2023

作者： Cao, Qumei Wen, Xuyun MIIT Key Laboratory of Pattern Analysis and Machine Intelligence Nanjing University of Aeronautics and Astronautics College of Computer Science and Technology Jiangsu Nanjing China

ISBN: (纸本)9798350331417

Predicting individual behavior from functional connectivity (FC) using machine learning is a critical research topic in neuroscience. While various models have been proposed, they mainly focus on designing behavior prediction methods, overlooking the influence of upstream FC network construction approach on downstream tasks. In this paper, leveraging graph neural networks (GNNs), we introduce an end-to-end deep learning framework named DouGNN, aimed at learning individual behavior from fMRI data. Through joint training of two GNNs, DouGNN innovatively integrates individualized cortical parcellation (for FC network construction) and individual behavior prediction into a unified optimization model. By integrating individualized cortical parcellation, the model gains the ability to dynamically modify the subject's cortical parcellation and the downstream tasks. This enhancement enables the subsequently constructed FC networks to better represent individual-specific features and task-related characteristics, thereby significantly improving the performance of individual behavior prediction. Moreover, the generated individual-specific, task-aware cortical parcellations from this process can also help us in understanding the relationship between FC and behaviors. We conducted three representative behavior prediction experiments on the publicly available HCP dataset. The results demonstrate that DouGNN outperforms existing methods in all behavior prediction tasks, achieving superior performance and generating more functionally homogeneous individualized cortical parcellations. © 2023 IEEE.

关键词： Behavior prediction functional magnetic resonance imaging graph neural network individualized cortical parcellation

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Review on Generative Adversarial Network in computer vision: Methods and Metrics 2

Review on Generative Adversarial Network in Computer Vision:...

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2nd international conference on Big Data and Artificial Intelligence and Software Engineering (ICBASE)

作者： Bao, Shuai Southwest Jiaotong Univ Chengdu Sichuan Peoples R China

ISBN: (纸本)9781665427098

Generate adversarial networks (GAN) is a popular method, which can be widely used in numerous research areas, such as computer vision, natural language processing, and time series synthesis. However, few references are proposed to give a comprehensive review on GAN based methods. This paper aims to provide a detailed review of GAN based algorithms on computer vision tasks, such as image style transfer, image/video generation, image matting, and image super-resolution. Furthermore, we conclude the evaluation metrics for those tasks to show the effectiveness of GAN based on method. Our review can help beginners recognize the GAN based methods and give a brief introduction on how to apply them to their tasks.

关键词： GAN image style transfer image matting image generation Video generation image super-resolution

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Automated Cell Tracking Using Adaptive Multi-stage Kalman Filter in Time-laps images 12

Automated Cell Tracking Using Adaptive Multi-stage Kalman Fi...

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12th Iranian/2nd international conference on Machine vision and image processing, MVIP 2022

作者： Naghshbandi, Hane Damavandi, Yaser Baleghi Babol Noshirvani University of Technology Faculty of Electrical and Computer Engineering Babol Iran

ISBN: (纸本)9781665412162

Segmenting living cells and tracking their movement in microscopy images are significant in biological studies and have played a crucial role in disease diagnosis, targeted therapy, drug delivery, and many other medical applications. Due to a large amount of time-lapse image data, automated image analysis can be a proper alternative to manual analysis, which is unreasonably time-consuming. However, Low-resolution microscopic images, unpredictable cell behavior, and multiple cell divisions make automated cell tracking challenging. In this paper, we propose a novel multi-object tracking approach guided by a two-stage adaptive Kalman forecast. Cell segmentation is performed using an edge detector combined with various morphological operations. The tracking section includes two general stages. At first, a Kalman filter with a constant speed is used to estimate the position of each cell in consecutive frames. The primary Kalman filter was able to detect a significant percentage of cells, but the high rate of cell division and migration of cells in or out of the field of view has caused errors in the final result. In the next stage, a secondary Kalman filter with modified parameters extracted from the results of initial tracking is proposed to estimate the position of cells in each frame, decrease errors, and improve the tracking results. Experimental results indicate that our method is 94.37% accurate in segmenting cells. The validity of the whole method has been conducted by comparing the results of the proposed method with manual tracking results, which demonstrates its efficiency. © 2022 IEEE.

关键词： Kalman filters

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A Comparative Analysis of Various Deep Learning Models for Traffic Signs Recognition from the Perspective of Bangladesh 2nd

A Comparative Analysis of Various Deep Learning Models for...

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2nd international conference on Big Data, IoT and Machine Learning, BIM 2023

作者： Tusher, Md. Mahbubur Rahman Kafi, Hasan Muhammad Rinky, Susmita Roy Islam, Muhiminul Rahman, Md. Musfiqur Bangladesh Army University of Science and Technology Saidpur Bangladesh United International University Dhaka Bangladesh

ISBN: (纸本)9789819989362

As they play a significant role in autonomous driving and traffic safety, traffic sign identification and recognition have recently emerged as one of the most significant fields in image processing and computer vision. Early studies in this field offered several deep learning-based methods for classifying distinct traffic signs using various standard datasets. However, not many researchers focused on creating a dataset of traffic signs in Bangladesh and applying deep learning techniques to recognize them. In this research, we compare and contrast several deep learning models for recognizing traffic signs from the perspective of Bangladesh. We construct a novel dataset with over 2000 images representing thirteen distinct kinds of typical traffic signs in Bangladesh. Using data augmentation, about 8386 images are generated from the original dataset. Subsequently, transfer learning and fine-tuning approaches are applied to nine different deep learning models using this dataset, and the outcomes are compared. Results indicate that ViT had the highest validation accuracy of 99.91% for fine-tuning, while DenseNet201 had the highest validation accuracy of 99.86% for transfer learning. Almost all models attained excellent training and validation accuracy levels, showing that they were able to successfully learn the dataset’s characteristics. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Traffic signs

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IRats:Intelligent system for rat behavior analysis 2

IRats:Intelligent system for rat behavior analysis

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2nd international Mobile, Intelligent, and Ubiquitous Computing conference, MIUCC 2022

作者： Zaky, Andrew Saber, Mona M. Essam, Reham M. Atia, Ayman Faculty of Computer Science Giza Egypt Faculty of Pharmacy Department of Pharmacology and Toxicology Giza Egypt Helwan University HCI-LAB Faculty of Computers and Artificial Intelligence Cairo Egypt

ISBN: (纸本)9781665466776

Rats' behavior analysis is fundamentally important in the medical field and pharmaceutical industry. Drugs and chemical compounds are given to mice and rats to measure their therapeutic effects. If the drug's effect is promising, it will be investigated in humans. Data extraction from experiments and classifying the trajectory of rats and mice will help scientific researchers speed up the development of new drugs for the community. We used image processing and computer vision to analyze one of the eminent rat behavior analysis experiments, which is Morris Water Maze (MWM). In the current work, we implemented two experiments, the first was testing more than one object detection and tracking algorithm on rats and mice behavior analysis. Second, we used three tracking algorithms, which were $P Point-Cloud Recognizer, Dynamic Time Warping (DTW), and FastDTW, to track the rat's path and classify its movements. The result of the first experiment was that a combination of CSRT (Channel and Spatial Reliability Tracking) and optical flow sparse would be more accurate in extracting the data. In addition, the second experiment showed the highest tracking algorithm was FastDTW, with an accuracy of 81% © 2022 IEEE.

关键词： Drugs Tracking Heuristic algorithms Rats Ubiquitous computing Mice Behavioral sciences

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