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Snail recognition Using YOLO 4

Snail Recognition Using YOLO

引用

4th IEEE international conference on Artificial Intelligence in Engineering and Technology, IICAIET 2022

作者： Borreta, Juan Ricardo I. Bautista, Justin A. Yumang, Analyn N. School of Electrical Electronics and Computer Engineering Mapúa University Manila Philippines

ISBN: (数字)9781665468374

ISBN: (纸本)9781665468374

Many species of snails inhabit different areas in the world. Some species have made their way to farmlands and the urban regions, surviving through eating plants and breeding unnoticed making them a cause for concern and a known threat to some crops. A study on snail detection has been previously conducted, but recognizing individual species for their risk has not yet been pursued. this study aims to develop a Tiny-YOLOv4 snail recognition system using a Raspberry Pi. the model focuses on four snail species subject to an input image processed through the system. the outputs show the image with the relevant bounding boxes and labels and notify a user through email for any recognitions. the system produced an overall accuracy of 92%, proving successful in the study's objectives and providing a basis for future literature. © 2022 IEEE.

关键词： computer vision

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Correction of Two Human Key-Points Estimations for Medical Applications 6

Correction of Two Human Key-Points Estimations for Medical A...

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6th IEEE international conference on pattern recognition and Artificial Intelligence, PRAI 2023

作者： Luo, Hao Rashid, Said Ramdhan Nie, Yuru Chen, Ancheng Li, Chuan Wu, Sophiann Zheng, Zhiwen Cheng, Zhuzhong Castaneda, Benjamin Peng, Bo Tian, Chao Wu, Zhe Southwest Petroleum University School of Computer Science Chengdu China University of Electronic Science and Technology of China School of Life Science and Technology Chengdu China Sichuan Cancer Hospital Department of Women's Health Chengdu China Canyon Crest Academy San Diego United States Pontificia Universidad Catolica Del Peru Institute of Omics and Applied Biotechnology Lima Peru

ISBN: (纸本)9798350325485

Anatomical key-points recognition is essential in many medical image analyses and clinical healthcare applications. Successfully identifying these anatomical key points provides multiple advantages, such as assisting medical experts in making treatment adjustments and offering information that helps to position surgical instruments at the appropriate locations. However, manual anatomical key-point recognition is subjective, slow, and time-consuming, especially when processing many medical images in clinical institutions. To overcome these limitations, this study aims to establish the correlation between human anatomical key points based on OpenPose and Baidu AI key-point detection techniques and the truth ground anatomical key-points marked by therapists in human medical images. this relationship will help to optimize the detection performance, reduce cost, decrease human error, and accelerate the process. the Sichuan Cancer Hospital provided five whole-body scan images obtained from a clinical CT scanner. A medical expert subsequently identified 14 anatomical key points from each scan. Finally, the datasets were reconstructed into 3-dimensional volume models to visualize whole-body skin models and the skeletons. the human-Annotated 14 key points were then used as ground truth compared to the computer vision techniques: OpenPose and Baidu AI. Both OpenPose and Baidu AI were found to have systematic offsets from the ground true reference points. these findings are reported in this work and can be used as a correction method. © 2023 IEEE.

关键词： computer vision

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Integrating Multi-Resolution U-Net for Morphological Segmentation with Multi-Scale processing in Shape recognition 4

Integrating Multi-Resolution U-Net for Morphological Segment...

引用

4th IEEE international conference on ICT in Business Industry and Government, ICTBIG 2024

作者： Dhabliya, Dharmesh Vigenesh, M. Reddy, B. Sindhu, M. Garg, Prateek Meena, Y.R. Vishwakarma Institute of Technology Department of Information Technology Pune India Karpagam Academy of Higher Education Department of Computer Science Engineering Coimbatore641021 India Chitkara University Centre of Research Impact and Outcome Punjab Rajpura140417 India Prince Shri Venkateshwara Padmavathy Engineering College Department of Cse Chennai127 India Chitkara University Chitkara Centre for Research and Development Himachal Pradesh 174103 India School of Engineering and Technology Department of Civil Engineering Karnataka Bangalore India

ISBN: (纸本)9798331518981

Biological research, medical imaging, and satellite image processing all rely heavily on morphological segmentation. When dealing with complicated geometries, different intensities, and noise, traditional approaches fail miserably. the U-Net architecture of convolutional neural networks (CNNs) has demonstrated promising results in capturing both local and global picture characteristics. Nevertheless, when dealing with intricate morphological structures, their performance can be enhanced. this research presents a new method for morphological segmentation using a multi-resolution U-Net, which improves upon the classic U-Net design by adding a multi-resolution framework. Because of this adjustment, the network can now interpret picture characteristics at many scales concurrently, picking up both detailed information and larger contextual cues. If the borders between regions are not clearly defined or if morphological structures show a lot of variability, this method will be very helpful. Multiple resolution branches flow into the network's encoder and decoder paths in the proposed multi-resolution U-Net design. the network receives feature maps of varied resolutions from each branch, which runs on a separate image scale. through the use of multi-scale feature fusion, the network is able to get a deeper understanding of the image's object hierarchy, leading to more accurate segmentation of smaller, more complex structures while still accurately representing larger regions. In benchmark datasets, the model outperforms other state-of-art segmentation techniques such as the classic U-Net in pixel accuracy along with other evaluation criteria such as Dice coefficient and Intersection of Union (IoU). this is particularly true in the medical images applications. © 2024 IEEE.

关键词： Convolutional neural networks

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Sports Video Analysis Algorithm Based on image Multiple processing Technology 4

Sports Video Analysis Algorithm Based on Image Multiple Proc...

引用

4th international conference on Advances in Electrical Engineering and computer Applications, AEECA 2023

作者： Zhang, Jianyu Wanjiang University of Technology Anhui Ma'anshan China

ISBN: (纸本)9798350308082

Considering the explosive growth in the amount of online video information in the Internet information era, and the fact that human actions in video are the main body of video information, there is a strong demand for mining in-depth information in video content. the application of multiple processing techniques for sports video images in sports research has further developed. this study builds a spatiotemporal dual attention network (ST-DAtt) recognition architecture based on the CNN-LSTM model and combined with the attention mechanism. this method utilizes features from multiple visual perception levels on CNN as input to a temporal model, and it uses different LSTM units to mine spatiotemporal context information. By introducing two attention models, the spatiotemporal saliency of the extracted features is enhanced. Finally, after feature dimensionality reduction, it enters the fusion model to fuse the output features of different networks, aiming to jointly enable decision-making capabilities at multiple levels and improve model recognition performance. the experimental results on multiple datasets demonstrate the effectiveness of the research model, as well as its strong spatiotemporal representation ability, fast model efficiency, and strong robustness. © 2023 IEEE.

关键词： Sports

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Lane line detection technology based on machine vision 4

Lane line detection technology based on machine vision

引用

4th international conference on Artificial Intelligence and Advanced Manufacturing, AIAM 2022

作者： Rui, Raoqing Donghua University Shanghai China

ISBN: (纸本)9781665463997

Lane detection is a key technology in automatic driving systems. Aiming at the problems of low accuracy and susceptible to interference in the traditional detection algorithm as for yellow lane lines, and poor recognition of curved lane lines by the traditional Hough transform. In this paper, a lane line detection method based on machine vision is proposed. Firstly, the white lane lines are identified in the gray space, the yellow lane lines are identified in the HSV space and the yellow lane lines and white lane lines are then merged. the interference is reduced by dividing the region of interest and Gaussian filtering. then, the edge of the lane line is identified by edge detection and the image is transformed into an aerial view by inverse perspective transformation. Finally, the sliding window polynomial fitting method is adopted to fit the lane lines. At the same time, a lane departure warning method based on calculating curve curvature and center deviation is proposed. © 2022 IEEE.

关键词： computer vision

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Research Advanced in Deep Learning Based image recognition 4

Research Advanced in Deep Learning Based Image Recognition

引用

4th IEEE international conference on Civil Aviation Safety and Information Technology, ICCASIT 2022

作者： Mu, Zhihan Mo, Zhibin Chongqing University of Technology Chongqing China Ohio State University ColumbusOH United States

ISBN: (数字)9781665467667

ISBN: (纸本)9781665467667

image recognition has already been a popular discussion widely studied in computer vision community, which aims to predict the category of each image to distinguish different images. the traditional image recognition methods struggle to process images of large order of magnitude, taking less time and yielding unsatisfactory results. In recent years, benifiting from the powerful feature expression ability of deep convolutional neural networks, both the recognization accuracy and speed of deep learning based mage recognition methods have made a breakthrough, which replacing the traditional methods and becoming the mainstream method of image recognition. In this paper, based on detailed literature research and analysis, we conduct a comprehensive survey on existing methods of image recognition based on deep learning. Specifically, we first introduce the preferential neural networks including self-encoder, convolutional neural network, etc. We then introduce the application of image recognition in various fields and give a series of experiments to analyze the achievement of different detection algorithms on some common datasets. Finally, we try to speculate on the possible future directions of image recognition to make it practical. © 2022 IEEE.

关键词： image recognition

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Pre and Post Harvesting using Deep Learning Techniques: A comprehensive study 4

Pre and Post Harvesting using Deep Learning Techniques: A co...

引用

4th international conference on Recent Trends in computer Science and Technology, ICRTCST 2021

作者： Patil, Nisha Deepak Tiwari, Rajesh Kumar Kumar, Ajit Jjt University Computer Science And Engineering Rajasthan India Rvs College Of Engineering And Technology Computer Science And Engineering Jamshedpur India

ISBN: (纸本)9781665466332

the paper aims to provide a comprehensive view about the use of deep learning Pre-harvest and Post-Harvest management in viticulture. Traditional approach targets manual aid in Pre and Post-harvest Quantitative and Qualitative analysis of post-harvest and pre-harvest management. the traditional approach has its disadvantages like an undue time consuming, Chances of manual errors and Recordkeeping maybe a voluminous task. the observational study which aims to eliminate technical discrepancies by using deep learning ensures benefits like AI-assisted algorithm and image processing ensures accuracy in data reading and analysis, automatic fruit quality monitoring reduces "Farm-to-Table"time provides better marketing quality for grapes and Optimum utilization of supply chains. We'll look at how machine literacy can be used in current vineyard operations and processes to yield industry-applicable quality. the recurrent neural network is a deep knowledge classifier that may be used to make a system that can identify grapes rested on their quality. Deep learning networks like AlexNet, GoogleNet, and VGG16 (Very Deep Convolutional Networks for Large-Scale Picture recognition) could enhance image segmentation and classification of grape bunches while also resolving approach negligence. the study concludes by demonstrating how deep literacy can be used for pre-and post-harvest operations in the viticulture industry. © 2022 IEEE.

关键词： computer vision

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Complex Texture Features Learned by Applying Randomized Neural Network on Graphs 11

Complex Texture Features Learned by Applying Randomized Neur...

引用

11th international conference on image processing theory, Tools and Applications (IPTA)

作者： Zielinski, Kallil M. C. Ribas, Lucas C. Scabini, Leonardo F. S. Bruno, Odemir M. Univ Sao Paulo Sao Carlos Inst Phys Sao Carlos SP Brazil

ISBN: (纸本)9781665469647

Since the 1960s, texture has become one of the moststudied visual attribute of images for analysis and classification tasks. Among many different approaches such as statistical, spectral, structural and model-based, there are also methods that rely on analyzing the image complexity and learning techniques. these recent approaches are receiving attention for its promising results in the past few years. this paper proposes a method that combines complex networks and randomized neural networks. In the proposed approach, the texture image is modeled as a complex network, and the information measures obtained from the topological properties of the network are then used to train the RNN in order to learn a representation of the modeled image. Our proposal has proven to perform well in comparison to other literature approaches in two different texture databases. Our method also achieved a high performance in a very challenging biological problem of plant species recognition. thus, the method is a promising option for different tasks of image analysis.

关键词： Complex Networks computer vision Feature Extraction Texture Analysis pattern recognition

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An improved Yolov8-based pest detection model for detecting elongate larvae 4

An improved Yolov8-based pest detection model for detecting ...

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4th international conference on computer Graphics, image, and Virtualization, ICCGIV 2024

作者： Yi, Zhoujian Huo, Zijie Lai, Yuqi Yin, Zhjian Li, Jun Yang, Zhen School of Information and Mechanical and Electrical Engineering Jiangxi Science and Technology Normal University Nanchang330000 China School of Education Jiangxi Science and Technology Normal University Nanchang330000 China CECEP Lattice Technology Co. Ltd Nanchang330000 China

ISBN: (纸本)9781510683242

Agriculture, an integral part of human existence, plays a central role in the economic and strategic sphere of every country. Crop productivity is of concern to all, as pests and diseases can significantly reduce yields or even lead to the death of crops. therefore, the detection of such threats is of utmost importance. With the advances in deep learning, the application of object detection algorithms to identify and monitor these problems has gained momentum. the present work uses the improved YOLOv8 algorithm as a foundation and introduces extensions that show improved performance on the widely recognized IP102 dataset intended for public insect images. © 2024 SPIE.

关键词： Object detection Agriculture Diseases and disorders Detection and tracking algorithms Convolution Data modeling Target detection computer vision technology Education and training Feature extraction

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1st international Virtual conference on Visual pattern Extraction and recognition for Cultural Heritage Understanding (VIPERC 2022) 1

1st International Virtual Conference on Visual Pattern Extra...

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1st international Virtual conference on Visual pattern Extraction and recognition for Cultural Heritage Understanding, VIPERC 2022

作者： Amelio, Alessia Montelpare, Sergio Ursino, Domenico InGeo University "G. d’Annunzio" Chieti-Pescara Viale Pindaro 42 Pescara65127 Italy DII Marche Polytechnic University Via Brecce Bianche 12 Ancona60131 Italy

this document introduces the 1st international Virtual conference on Visual pattern Extraction and recognition for Cultural Heritage Understanding (VIPERC 2022), a premier forum for presenting the state-of-the-art, new research, ongoing work, academic and project reports in advanced statistics and machine learning, 3D modelling and simulation, knowledge representation, intelligent systems, information retrieval and software engineering, for visual pattern extraction, analysis and recognition to preserve the cultural heritage. © 2022 Copyright for this paper by its authors.

关键词： 3D modeling

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