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检索条件"任意字段=2024 3rd International Conference on Image Processing, Object Detection, and Tracking, IPODT 2024"

共 128 条记录，以下是121-130 订阅

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Rosette Plant Centre detection and tracking Using YOLO: An Efficient Deep Learning Approach

Rosette Plant Centre Detection and Tracking Using YOLO: An E...

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Computing and Machine Intelligence (ICMI), international conference on

作者： Amila Akagić Rijad Sarić Emir Buza Stefani Kecman Mathew G. Lewsey Edhem Čustović James Whelan Faculty of Electrical Engineering University of Sarajevo (UNSA) Sarajevo Bosnia and Herzegovina Dept. of Animal Plant and Soil Sciences La Trobe Institute for Sustainable Agriculture & Food (LISAF) Melbourne VIC Australia Australian Research Council Research Hub for Medicinal Agriculture La Trobe University Melbourne VIC Australia Scientific Instruments Australia (SIA) Melbourne VIC Australia State Key Laboratory of Plant Environmental Resilience College of Life Sciences Zhejiang University Hangzhou China Provincial International Science and Technology Cooperation Base on Engineering Biology Zhejiang University Haining China

ISBN: (数字)9798350372977

ISBN: (纸本)9798350372984

The precise detection of plant centres is important for growth monitoring, enabling the continuous tracking of plant development to discern the influence of diverse factors. It holds significance for automated systems like robotic harvesting, facilitating machines in locating and engaging with plants. In this paper, we explore the YOLOv4 (You Only Look Once) real-time neural network detector for plant centre detection. Our dataset, comprising over 12,000 images from 151 Arabidopsis thaliana accessions, is used to fine-tune the model. Evaluation of the dataset reveals the model's proficiency in centre detection across various accessions, boasting an mAP of 99.79% at a 50 % IoU threshold. The model demonstrates real-time processing capabilities, achieving a frame rate of approximately 50 FPS. This outcome underscores its rapid and efficient analysis of video or image data, showcasing practical utility in time-sensitive applications.

关键词： YOLO Measurement Visualization Neural networks Streaming media Real-time systems Robots

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Harnessing the Power of CNN to Detect Late and Early Blight in Potato with Enhanced Accuracy

Harnessing the Power of CNN to Detect Late and Early Blight ...

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Advancement in Technology (ICONAT), international conference for

作者： Kaustubh Tripathi Neeti Taneja Ibrar Ahmed Dhruv Kumar Soni Ashu Taneja Department of Computer Science and Engineering Noida Institute of Engineering and Technology Greater Noida Uttar Pradesh India Chitkara University Institute of Engineering and Technology Chitkara University Punjab India

ISBN: (数字)9798350354171

ISBN: (纸本)9798350354188

Potatoes are a ubiquitous vegetable in every household. Potato planting has been widespread in India for the past decades. However, potato output is being impeded by diseases such as late blight and early blight [1], which result in higher production costs. The objective of this project is to develop a rapid disease detection procedure using deep learning techniques. The goal is to diagnose diseases in potato crops at an early stage, hence increasing potato production. The diagnosis will be done using digital methods. Our primary objective is to identify and diagnose potato diseases by utilizing leaf images that have been enhanced and trained using the Convolutional Neural Network (CNN) technique. This study provides a comprehensive explanation of image processing and deep learning algorithms used in automated systems to detect and classify potato leaf diseases. image processing is the optimal choice for the detection and diagnosis of agricultural illnesses. This study involves the division of around 2100 images of healthy and afflicted potato leaves, which were obtained via Kaggle and other pre-prepared models. The program forecasts an accuracy of 97.27% when tested with 20% test and validation data and 80% training data. Our results demonstrate that we successfully completed all essential duties in detecting potato diseases.

关键词： Deep learning Accuracy image processing Operating systems Training data Crops Production Convolutional neural networks object recognition Diseases

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Contextual Transformer Based Small Targets detection for Cervical Cell

Contextual Transformer Based Small Targets Detection for Cer...

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image processing and Media Computing (ICIPMC), international conference on

作者： Juanjuan Yin Qian Zhang Wenjing Lu Jinye Peng Jun Wang Xiaohui Li School of Information Science and Technology Northwest University Xi’an China State-Province Joint Engineering and Research Center of Advanced Networking and Intelligent Information Services School of Information Science and Technology Northwest University Xi’an China College of Chemistry and Chemical Engineering Xi’an Shiyou University Xi’an China

ISBN: (数字)9798350386660

ISBN: (纸本)9798350386677

Cervical cancer poses a significant threat to women’s health as a malignant tumor. Currently, liquid-based thin-layer cytology stands as the predominant method for detecting cervical cancer cells. The conventional approach to identifying cervical cell pathology images relies heavily on skilled medical professionals, a process known for its time-consuming and labor-intensive nature, along with its inherent limitations. Although the integration of deep learning and imaging has demonstrated promising results in medical auxiliary diagnosis, existing methods for small object detection encounter challenges in effective feature extraction and the balanced representation of positive and negative samples. To address the aforementioned issues, we propose an optimized model for cervical cell detection that bolsters the model’s capability for extracting features related to small targets by leveraging a fusion of context and self-attention mechanisms. Our approach employs an Adaptive Training Sample Selection (ATSS) strategy for Anchors to ensure a proportionate representation of positive and negative samples. Furthermore, we incorporate a softer Soft-NMS algorithm to prevent the abrupt elimination of small targets and utilize Focal Loss to steer the model towards prioritizing small target identification. Experimental evaluations conducted on a publicly accessible cervical cell dataset validate the efficacy of our method. The model achieves an mAP value of 75.5%, showcasing a notable 7.9% enhancement in the detection AP value for cells falling within the small target category, surpassing other models of a similar nature.

关键词： Training Adaptation models Accuracy Computational modeling object detection Feature extraction Transformers

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Vehicle detection and Speed Estimation using Deep Convolutional Neural Network

Vehicle Detection and Speed Estimation using Deep Convolutio...

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Automation, Computing and Renewable Systems (ICACRS), international conference on

作者： U. Sakthi Imthiyaaj Mynudeen Praveen Kumar G M Department of Computational Intelligence SRM Institute of Science and Technology Chennai TN India

ISBN: (数字)9798331532420

ISBN: (纸本)9798331532437

This research study intends to detect the velocity of moving objects by means of video recording, advanced digital image processing, and deep convolutional neural network. Approach towards the development of an accurate estimation of high-precision speed for vehicles in traffic flows will be proposed. The main contribution of this work is the utilization of the Gaussian Mixture Model (GMM) for background subtraction, which will distinguish the moving objects from the stationary ones. Density- Based spatial clustering of application with noise is applied to cluster and detect vehicles in crowded situations in which they can merge or become separated. Vehicle position prediction and error correction made use of the Kalman filter, following vehicle tracks across video frames. High accuracy was captured estimating the speed without occlusions or sudden changes, and Optical Flow was also used for motion vector fields, where it would be able to capture dynamics in vehicle motion, for example, when changing lanes or turns. The architecture is modular; it consists of preprocessing, detection, track processing modules, and speed computation modules with well-defined communication protocols in between them. This makes it relatively easy to upgrade later with additions like vehicle classification or anomaly detection while keeping a strong data flow and intermediate result refinement intact.

关键词： Adaptation models Analytical models Accuracy tracking Estimation Real-time systems Data models Convolutional neural networks Monitoring Video recording

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Research on the surface topography inspection of sandpaper based on machine vision

Research on the surface topography inspection of sandpaper b...

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Innovations and Development of Information Technologies and Robotics (IDITR), international conference on

作者： Huifeng Zheng Jinru Liu Ge Chen Lei Xiao College of Mechanical Engineering Donghua University Shanghai China

ISBN: (数字)9798350385694

ISBN: (纸本)9798350385700

To solve the problems of low efficiency and high subjectivity of existing sandpaper morphology parameter detection methods, machine vision methods are used to detect the surface morphology characteristics of sandpaper. A weighted decision matrix is established to select the important parameters that can reflect the topography of the sandpaper surface; Zeiss Smartproof5 fast turntable confocal microscope equipment is used to obtain the true-color view of different sandpaper samples, the pseudo-color view based on the height, and the XYZ three-dimensional point cloud of the sandpaper surface; the image is preprocessed by the methods of threshold segmentation and morphology processing, and then segmented by watershed segmentation; and the key topographic parameters of different sandpaper samples are calculated and compared. The key morphological parameters of different sandpaper samples were calculated and compared. The results show that the number of abrasive grains, abrasive grain size and other parameters increase with the increase of mesh size, while the uniformity of sandpaper distribution decreases with the increase of mesh size. The results of this study can provide a theoretical reference for the realization of automated inspection of sandpaper surface topography.

关键词： Grain size image segmentation Microscopy Surface morphology Morphology object segmentation Watersheds

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Deep Learning based vehicle image detection using Yolo V5 with Region-Based Convolutional Neural Network

Deep Learning based vehicle image detection using Yolo V5 wi...

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Artificial Intelligence For Internet of Things (AIIoT), international conference on

作者： S. Swapna Rani Aditya Mudigonda S V Hemanth P.N. Sundararajan G. Vinoda Reddy G. Amirthayogam Department of Electronics and Communication Engineering Maturi Venkata Subba Rao (MVSR) Engineering College Hyderabad India JNIAS School of Planning and Architecture Hyderabad Telangana India Department of Computer Science and Engineering Hyderabad Institute of Technology and Management Hyderabad Telangana Department of Electronics and Communication Engineering PSNA College of Engineering and Technology Dindigul Tamil Nadu India Department of Computer Science and Engineering (AI&ML) CMR Technical Campus Kandlakoya Hyderabad Telangana India Department of Information Technology Hindustan Institute of Technology and Science Chennai Tamilnadu India

ISBN: (数字)9798350372120

ISBN: (纸本)9798350372137

Detecting and classifying vehicles is a modern technology with numerous uses. Administration and regulation of traffic is one of the primary uses. Projects utilizing image processing to prevent traffic accidents heavily rely on vehicle monitoring and detection. Monitoring and recording human movement in surveillance situations require the ability to follow moving objects. Considering its significance, it provides a valuable image processing-based vehicle detection technique—a vehicle tracking and detection system based on images. The recently released high-resolution road vehicle dataset supports deep learning-based vehicle detection and monitoring on the Python platform. It includes over 100 well-defined pictures taken from movies from various locations. This data is produced using a range of image processing methods. The most recent iteration of the YOLO model, the v5 model, was employed for detection. R-CNN is additionally utilized for model detection. The four stages in the Region-Based Convolutional Neural Network (RCNN) process are preprocessing, segmentation, feature selection, and classification. The first step is identifying moving vehicles accurately. Preprocessing the original image is necessary. This enhances the image’s quality, valuable information extracted, or unwanted areas cropped out. Segmentation is the second step. The region’s borders are established, and the entire image is divided into numerous smaller areas using a threshold. The vehicle image’s most pertinent aspects or characteristics (features) that aid in precise detection are found in the third stage, feature selection. A region-based convolutional neural network (RCNN) is employed to determine the categorization. The Yolov5 method can be used to identify and categorize items. The Region-based Convolutional Neural Network (RCNN) approach enhances accuracy and temporal complexity over current segmentation algorithms.

关键词： YOLO Deep learning image segmentation Accuracy Surveillance Transportation Transforms

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Comparison between 2D Classification and 3D Classification based on Deep Learning

Comparison between 2D Classification and 3D Classification b...

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Embedded Systems and Artificial Intelligence (ESAI), international conference on

作者： Zouhir El Khalfi Soulaiman El Hazzat Majid Ben Yakhlef LSI Department of Computer Science Polydisciplinary Faculty of Taza Sidi Mohamed Ben Abdellah University Fez Morocco

ISBN: (数字)9798331522056

ISBN: (纸本)9798331522063

The rise in processing power of computers and the availability of a considerable amount of data have allowed the overlap of several machine learning approaches. This has led to breakthroughs on many deep learning tasks as speech recognition, natural language processing (NLP), object detection and image analysis. Thus, many fields as autonomous driving, industrial quality processes and mobile robotic have released multiples researches in deep learning algorithmic topics. In this paper, we have made a comparison, in terms of accuracy and process timing, between classifications based on 3D points cloud and 2D images for the same input dataset in raw format, and generated basic outlines of both deep learning methods using algorithms based on convolution networks. We discovered that 3D points cloud based classification model was slow on training stage than 2D images model timing due to the 3D complexity data representation. Besides that, classification neural networks based on 3D points cloud data performed better on accuracy than the networks based on 2D images datasets. It is to highlight that 3D based systems are so critical in terms of data processing compared to 2D images due to: Data format as points cloud, meshes and volumes, the hardware resources requirement which is higher due to the third added dimension. Finally, we hypothesize that systems which would combine both of these data geometry and networks architectures would return accuracy better and low processing time wise.

关键词： Point cloud compression Deep learning Solid modeling Three-dimensional displays Accuracy Convolution Natural language processing Data models Timing Classification algorithms

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A Comprehensive Approach to Enhancing Science Practical Education using AI, ML, image processing, and NLP for Secondary Education

A Comprehensive Approach to Enhancing Science Practical Educ...

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Automation, Computing and Renewable Systems (ICACRS), international conference on

作者： K. P. P. U Ranasinghe J. S Ranasinghe D. C Wickramarachchi W. P. D. B Karunarathna Samantha Rajapaksha Vishan Jayasinghearachchi Department of Information Technology Sri Lanka Institute of Information Technology Sri Lanka Sri Lanka Institute of Information Technology Sri Lanka

ISBN: (数字)9798331532420

ISBN: (纸本)9798331532437

Practical science education is crucial for developing students’ understanding of core scientific concepts, yet many secondary students face challenges due to inadequate school resources. This often results in lower academic performance and diminished interest in science. To address these issues, we propose the development of an innovative online platform designed to enhance science practical education by integrating four advanced technologies: emotion detection, hand gesture recognition, object identification, and chatbot support. This platform offers an interactive and engaging approach to learning by leveraging technology to overcome the limitations of traditional classroom resources. The emotion detection feature monitors students’ emotional responses as they interact with science-related practical questions. By analyzing emotional cues such as confusion or frustration, the platform delivers personalized content to address areas where students struggle. The hand gesture recognition feature presents multiple-choice questions and allows students to respond by showing the number of their answer choice with their fingers, promoting quick thinking and improving calculation speed. object identification enables students to upload images of objects from the science curriculum, which the platform then identifies and explains, helping students relate theoretical knowledge to real-world examples. Additionally, the platform’s chatbot provides real-time support, answering students’ questions on practical topics and guiding them through difficult concepts. Through this comprehensive approach, our platform bridges the gap in science practical education by providing students with an accessible, personalized, and interactive learning experience. This solution not only enhances their understanding of scientific principles but also fosters academic success and a deeper engagement with the subject matter.

关键词： Hands Emotion recognition Renewable energy sources Education Refining Gesture recognition Chatbots Real-time systems object recognition Monitoring

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