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Detecting Raspberry Ripeness Across Different Growth Stages Using YOLOv8 4th

Detecting Raspberry Ripeness Across Different Growth Stages ...

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4th international conference on Intelligent Systems and pattern recognition-ISPR

作者： Alkhudaydi, Tahani Univ Tabuk Dept Comp Sci Tabuk 71491 Saudi Arabia

ISBN: (纸本)9783031821523;9783031821530

Raspberries, valued at $1.5 billion in 2018 face challenges in cultivation especially in monitoring growth stages. Manual monitoring is labor intensive and impractical which can lead to poor crop management and productivity issues. the use of computer vision technology, particularly, the YOLO algorithm known for its real time object detection performance has transformed agriculture by enabling efficient crop monitoring. However its application in detecting raspberries at different growth stages is still relatively unexplored. this study presents a comprehensive analysis of four YOLOv8 variations to identify and locate raspberries by their ripeness levels. It focuses on the RaspberrySet, a challenging dataset with densely overlapped raspberries from various growth stages. through experiments involving 15 configurations of YOLOv8 we were able to determine the effective hyperparameters for this task. the results indicate that tuning specific hyperparameters significantly enhances classification accuracy with all variants of YOLOv8 outperforming the baseline model by an average of 5.91%. the YOLOv8l model emerged as the accurate among them achieving the mAP50 and mAP50 95 scores at 78.6% and 47.5%, respectively. However it struggles when it comes to identifying damaged buds due to insufficient training data. Despite its accuracy, the YOLOv8l model exhibited the longest inference time which indicate the need for faster and more efficient models suitable for deployment in agricultural environments. the next phase of research aims to develop a lightweight model that leverages YOLOv8l knowledge to enhance performance while maintaining acceptable speed.

关键词： YOLOv8 Yield estimation Crop Monitoring Raspberry Deep learning

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Development of Low Resource machine learning Models for Child Cognitive Ability Assessments 4

Development of Low Resource Machine Learning Models for Chil...

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4th international conference on Advancements in Computing, ICAC 2022

作者： Kahawanugoda, Adithya Gnanarathna, Kulanika Meegoda, Nilan Monarawila, Randika Samarasinghe, Pradeepa Lindamulage, Asiri Gawesha Department of Computer Science and Software Engineering Faculty of Computing Sri Lanka Institute of Information Technology Malabe Sri Lanka Department of Information Technology Faculty of Computing Sri Lanka Institute of Information Technology Malabe Sri Lanka

ISBN: (纸本)9798350398090

Automated cognitive assessment tools are state-of-the-art in assessing cognition development. Due to the low availability of resources, building automated cognitive ability evaluation tools is challenging. this study focuses on developing machine learning models using a limited amount of data to assess Reasoning IQ, Knowledge IQ, Mental Chronometry and Attention-levels of Sinhala-speaking children between the age of 7 to 9 years. Our solution includes Sinhala speech recognition systems, image classification models, gaze estimation, blink count detection and facial expression recognition models to evaluate the above four cognitive ability measuring factors. Open domain speech recognition has been used to evaluate complex Sinhala child verbal responses and limited vocabulary responses were assessed using an end-to-end speech recognition system, respectively achieving 40.1% WER and 97.14% accuracy. Additionally, the image classification models for handwritten Sinhala letter recognition and two shape recognition models have gained 97%, 89% and 99% accuracy. the linear regression model for attention level evaluation that utilizes the inputs from a combination of eye-gaze estimation, facial expression recognition and blink rate detection models has gained 85% accuracy. © 2022 IEEE.

关键词： Image classification

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Privacy and data Control on Social Networks using Deep learning 4

Privacy and Data Control on Social Networks using Deep Learn...

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4th international conference on Cybernetics, Cognition and machine learning Applications, ICCCMLA 2022

作者： Parimala, A. Vijayalata, Y. Gokaraju Rangaraju Institute of Engineering and Technology Dept. of Computer Science and Engineering Telangana Hyderabad India

ISBN: (数字)9781665462464

ISBN: (纸本)9781665462464

Online social networks are huge data exchange platforms that help to promote and share a lot of good information about products, news, education, tourism, health care, etc., also there is a great risk involved to individual's privacy and security. Online posted photos can be shared, tagged, and reposted again without having any consent from the people present in the images. In this project, we are proposing a facial recognition-based system that detects every single individual and checks their online account relationship between the individual. the post or image that is reposted, tagged, or copied by known or unknown individuals will be checked. If it is an unknown individual the images convolved through a low-pass filter kernel which removes the high-frequency content like noise and edges to smooth the images that make the image blur. For this mechanism, we proposed to use deep learning based VGG19 architecture for image classification and Haar cascade viola jones algorithm for detection, and computer vision-based blurring techniques used. algorithm performance as the area under the ROC curve (AUC) accuracy of 88% obtained. this proposed model achieved 91.53% classification accuracy and further we evaluated the model using Accuracy, Precision, Recall and F1-Score metrices and achieved 70.83%, 82%, 71% and 0.68%. © 2022 IEEE.

关键词： Deep learning

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A Method for Protecting Ceramic pattern Property Rights Based on ResNet50 4

A Method for Protecting Ceramic Pattern Property Rights Base...

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4th international conference on Computer Science and Management Technology, ICCSMT 2023

作者： Zhang, Hongwei Peng, Yongkang Liao, Shuhong Mao, Jingrong School of Information Engineering Jingdezhen Ceramic University Jingdezhen Jiangxi China Guangdong Songfa Ceramics Company Limited Chaozhou Guangdong China

ISBN: (纸本)9798400709517

the similarity detection of ceramic patterns is of great research significance in protecting the original intellectual property rights of ceramic patterns and preventing piracy. the goal of this article is to study a deep learning based method for calculating the similarity of ceramic patterns to determine whether they are infringing patterns that are forged or plagiarized from others' works. therefore, this article adopts the mainstream deep residual network (ResNet50) model in the field of image recognition for feature extraction and calculates the cosine value of the feature vectors of two images to determine similarity. At the same time, experiments were conducted to compare the method used in this article with traditional hash algorithms and commonly used SIFT algorithms. the experimental data results showed that feature extraction based on ResNet50 was more robust, and the calculated cosine values of ceramic pattern features were more accurate, both reaching 0.8 or above, with a higher accuracy in distinguishing similarity. Overall, a more accurate and effective new method is provided to address the issue of plagiarism of ceramic pattern works from others without penalty due to local or color changes. © 2023 ACM.

关键词： Feature extraction

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Research and Application of Fault Diagnosis and Localization Techniques for Power Communication Optical Cables Based on Multimodal data Analysis

Research and Application of Fault Diagnosis and Localization...

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data Science and Computer Application (ICDSCA), IEEE international conference on

作者： Xuguang Zhang Bo Zhang Bo Guo Hang Ren Wanxin Wang Chao Ma State Grid Jilin Electric Power Co. Ltd. Tonghua Power Supply Company Jilin China

ISBN: (数字)9798350368239

ISBN: (纸本)9798350368246

Aiming at the problems of low accuracy of power cable fault pattern recognition and large error of fault location, a method of power cable fault diagnosis and location based on Beluga Whale Optimization-Extreme learning machine (BWO-ELM) algorithm and GIS system is proposed. First, the beluga whale optimization (BWO) algorithm is used to optimize the initial parameters of the extreme learning machine (ELM), and the BWO-ELM multi classification Optical Time Domain Reflectometry (OTDR) curve analysis fault pattern recognition method is constructed, obtain the straight-line distance and type of the optical fiber fault point to lay the foundation for fault location. Secondly, the accurate positioning method of optical cable fault based on GIS is proposed, which converts the straight-line distance of the optical fiber fault point into the optical cable distance, and matches it with the actual geographical location to obtain the coordinates of the actual fault point, which are intuitively displayed in the GIS system. Finally, the application effect of the proposed method is verified by simulation experiments. the results show that the proposed method achieves a fault pattern recognition accuracy of about 98.66%, the fault location error is floating up and down 3M, and the average error is 1.481%. It has higher performance and accuracy than other recognition models and fault location methods.

关键词： Optical fibers Integrated optics Accuracy Power cables Optical fiber cables Fault location Adaptive optics pattern recognition Optical reflection Geographic information systems

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Revealing the Unseen: A Single-Stage Attention Based Occluded Object Detection Model in Remote Sensing Imagery 10th

Revealing the Unseen: A Single-Stage Attention Based Occlude...

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10th Biennial international conference on pattern recognition and machine Intelligence (PReMI)

作者： Saini, Nandini Chattopadhyay, Chiranjoy Das, Debasis IIT Jodhpur Comp Sci & Engn Jodhpur 342037 India FLAME Univ Sch Comp & Data Sci Pune 412115 India

ISBN: (纸本)9783031451690;9783031451706

Object detection based on deep learning has achieved promising results on traditional datasets, but detecting objects in remote sensing imagery with diverse occlusions remains challenging. this is due to the fact that natural occlusions are common in real-world images, and there is a lack of adequate datasets and neglect of latent information that can be useful for identification. Our research endeavors to tackle this challenge by presenting an innovative single-stage image-adaptive YOLO-transformer framework that leverages attention-based mechanisms to enable the model to concentrate on important regions and extract more distinctive features. To optimize the model's accuracy while maintaining its lightweight and suitability for real-time applications, we employed a depthwise convolution and SiLU activation function in lieu of the standard convolution. these modifications allow our framework to attain high levels of precision. Our approach is evaluated on synthetically generated occluded datasets from publicly available NWPU-VHR10 and demonstrated adaptive processing of images in both normal and three types of environmental occlusions (foggy, rainy, and cloudy). the experimental results are highly promising, as our approach achieved an inference speed of 6 ms with 11.9 GFLOPs on the NVIDIA Tesla T4 while maintaining effectiveness in all three occlusion types.

关键词： Object Detection Environmental Occlusion Remote Sensing Images CNN-Transformer Attention Mechanism

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data Processing Technology for Network Abnormal Traffic Detection 4

Data Processing Technology for Network Abnormal Traffic Dete...

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4th international conference on machine learning and Big data Analytics for IoT Security and Privacy, SPIoT 2023

作者： Wang, Kun Fu, Yu Duan, Xueyuan Xu, Jianqiao Liu, Taotao Department of Information Security Naval University of Engineering Wuhan430033 China School of Mathematics and Information Engineering Xinyang Vocational and Technical College Xinyang464000 China College of Computer and Information Technology Xinyang Normal University Xinyang464000 China Henan Key Laboratory of Analysis and Applications of Education Big Data Xinyang Normal University Xinyang464000 China

At present, the threats to network security are also increasing, among which abnormal traffic detection is the key link to ensure network security. Traditional detection methods based on signature or threshold are often difficult to adapt to the increasingly complex network environment and new attack methods. therefore, this paper optimizes and improves the data processing technology, proposes a network ATD method based on particle swarm optimization(PSO) algorithm, and explores in detail the traffic data collection and pre-processing, the feature recognition of abnormal traffic, the application of PSO algorithm, real-time monitoring and response mechanism. the results of two sets of simulation experiments are as follows: compared with the traditional model, the accuracy rate of ATD of the improved algorithm is increased by 7.2% on average, and the detection time is reduced by 7.35s on average. this method not only enhances the adaptability of the model to new attacks, but also improves the degree of automation of detection. © 2024 the Authors.

关键词： Particle swarm optimization (PSO)

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Stop Overkilling Simple Tasks with Black-Box Models, Use More Transparent Models Instead 4th

Stop Overkilling Simple Tasks with Black-Box Models, Use Mo...

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4th international conference on pattern recognition and Artificial Intelligence, ICPRAI 2024

作者： Rizzo, Matteo Marcuzzo, Matteo Zangari, Alessandro Schiavinato, Michele Albarelli, Andrea Gasparetto, Andrea Department of Environmental Sciences Informatics and Statistics Ca’ Foscari University of Venice VE Mestre30172 Italy

ISBN: (纸本)9789819787012

the ability of deep learning-based approaches to extract features autonomously from raw data while outperforming traditional methods has led to several breakthroughs in artificial intelligence. However, these models suffer from intrinsic opacity, making it difficult to explain their predictions. this is problematic not only because it hinders debugging but, most importantly, because it negatively affects the perceived trustworthiness of the systems. What is often overlooked is that many relatively simple tasks can be solved efficiently and effectively with data processing strategies paired with traditional models that are inherently more transparent. this work highlights the frequently neglected perspective of using knowledge-based and explainability-driven problem-solving in ML. We introduce a set of guidelines to design explainable models and apply them to the task of classifying the ripeness of banana crates. this is done by planning explainability and model design together. We showcase how the task can be solved using opaque deep learning models and more transparent strategies. Notably, there is a minimal loss of accuracy but a significant gain in explainability, which is truthful to the model’s inner workings. Finally, we perform a user study to evaluate the perception of explainability by the end users and discuss our findings. © the Editor(s) (if applicable) and the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Contrastive learning

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Analyzing and Comparing Results for Multiple machine learning Models 4

Analyzing and Comparing Results for Multiple Machine Learnin...

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2023 4th international conference on Computer Engineering and Intelligent Control, ICCEIC 2023

作者： Ng, Jefferson Li, Yijian Ji, Xuxiao Yang, Can Wan, Jiaqiao Lu, Xingyu University of California Department of Mechanical Engineering Irvine92697 United States Courant Institute of Mathematical Sciences New York University NY10012 United States University of Waterloo School of Computer Science WaterlooCAN2L 3G1 United States Oregon State University Department of Electrical Engineering and Computer Science Corvallis97331 United States Zhengzhou University Software College Zhengzhou450000 China United World College Changshu China Jiangsu215500 China

ISBN: (纸本)9798350308877

In the contemporary landscape of atmospheric sciences, the ability to recognize specific weather phenomena accurately and efficiently has become paramount, largely due to the exponential growth in sensor-generated data. this paper undertakes an in-depth analysis of advanced algorithms, namely YOLOV8, ResNet50, and Convolutional Neural Networks (CNN), for the purpose of weather pattern identification and classification. Building on the foundation of deep learning, the YOLOV8's real-time object detection capabilities are leveraged to discern intricate weather patterns in diverse datasets, from meteorological stations to satellite imagery. On the other hand, Resnet 50 which is another CNN based model was also tested along with CNN for comparison. An exhaustive evaluation of these algorithms covers various metrics, including accuracy, precision, computational efficiency, and real-world applicability. Special emphasis is placed on the crucial stages of data preprocessing, feature extraction, and model tuning, highlighting their impact on the algorithms' overall performance. Our findings suggest that, when appropriately optimized, both YOLOV8 and CNN exhibit exceptional capabilities in discerning and classifying intricate weather patterns, whereas ResNet50 exhibits comparatively less performance. © 2023 IEEE.

关键词： Convolutional neural networks

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A Comprehensive Study of Road Traffic Accidents: Hotspot Analysis and Severity Prediction Using machine learning 4

A Comprehensive Study of Road Traffic Accidents: Hotspot Ana...

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4th IEEE Bombay Section Signature conference, IBSSC 2022

作者： Gupta, Utkarsh Varun, M.K. Srinivasa, Gowri Pes University Dept. of Computer Science and Engineering Bengaluru India Pes University Pes Center for Pattern Recognition Dept. of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9781665492911

this study analyses road traffic accident data recorded over a period of time to gain insights to the underlying pain points in the infrastructure and policies. Such insight allows us to focus our efforts in the right direction to make the lives of people safer. the data includes various geographical and meteorological factors affecting the severity of these accidents. We use Kernel density estimation (KDE) plots to analyse hotspots of accident-prone areas weighed against severity over years to understand the evolution of these dangerous zones. Furthermore, we use machine learning algorithms to predict the accident severity given certain parameters and to understand the factors that have a major influence on the severity of the accident. We have studied a publicly available dataset of road traffic accidents in the UK as a proof of concept of the pipeline to understand the underlying patterns of accidents occurring in a region of interest. © 2022 IEEE.

关键词： machine learning

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