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IOA-YOLO: detection of illegal overhead cables based on linear enhancement and dual perception

SIGNAL image AND VIDEO processing 2025年第8期19卷 1-11页

作者： Wu, Yujie Dai, Jiguang Zhang, Tengda Ma, Zheng Liaoning Tech Univ Sch Geomat Yulong St Fuxin 123000 Liaoning Provin Peoples R China

real-time and accurate detection of overhead cables violating street-level regulations is crucial for smart city management. Existing methods face challenges like slender target nature, occlusion, multi-scale variability, and high inter-class similarity. This paper presents the IOA-YOLO model. It incorporates a Line-Target Enhancement Module (LTEM) for better slender object feature extraction, a Global-Local Dual Perception Module (GDPM) to boost robustness against occlusion, and a Hybrid Iterative Detection Head (HIDH) for multi-scale feature extraction using intra-and inter-layer information. An uncertainty-aware loss function (UAL) is introduced to suppress background interference and reduce inter-class similarity impact. Experiments on a custom dataset show IOA-YOLO outperforms existing methods, achieving 93.94% precision and 88.17% recall, with a good balance between accuracy and efficiency. It also adapts well to various urban environments and lighting conditions, demonstrating robust stability and great real-world deployment potential.

关键词： Disorderly overhead cables along the street deep learning Street view images Objection detection

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Vision Sense: real-time Object Detection And Audio Feedback System For Visually Impaired Individuals 2

Vision Sense: Real-Time Object Detection And Audio Feedback ...

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2nd IEEE World Conference on Communication and Computing (WCONF)

作者： Chinni, Naga Praneeth Kumar Kaamaala, Sai Pranav Reddy Vardhan, Bhasham Vishva Kishan, Adari Uday Richards, Vasimalla Sunny Vardhan, Ramadasu Nooka Harsh Puneet Lovely Profess Univ Comp Sci & Engn Jalandhar Punjab India

ISBN: (纸本)9798350395334;9798350395327

This project introduces a transformative object detection system designed to enhance the navigational capabilities of visually impaired individuals through the application of advanced computer vision technologies. Utilizing the You Only Look Once (YOLO) model, paired with the Comprehensive Object Collection (COCO) dataset, this system provides real-time, accurate object detection and classification. The core functionality of the application allows for the processing of both static images and live video feeds, enabling blind users to receive auditory announcements of nearby objects, thereby assisting with spatial awareness and environmental interaction. The system leverages a pre-trained YOLO model to ensure robust detection performance, achieving a peak detection accuracy of 99%. By delivering object labels and bounding box coordinates audibly, the application serves as a critical tool in improving the daily independence and quality of life for people with visual impairments. This project not only highlights the potential of deep learning in assistive technologies but also underscores the importance of adaptive solutions in inclusive technology development.

关键词： YOLO model COCO dataset object detection visually impaired assistance real-time processing audio feedback computer vision assistive technology

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Cervical Cancer Classification From Pap Smear images Using deep Convolutional Neural Network Models

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INTERDISCIPLINARY SCIENCES-COMPUTATIONAL LIFE SCIENCES 2024年第1期16卷 16-38页

作者： Tan, Sher Lyn Selvachandran, Ganeshsree Ding, Weiping Paramesran, Raveendran Kotecha, Ketan UCSI Univ Inst Actuarial Sci & Data Analyt Jalan Menara Gading Kuala Lumpur 56000 Malaysia Monash Univ Malaysia Sch Sci Jalan Lagoon Selatan Bandar Sunway 47500 Subang Jaya Malaysia Symbiosis Int Univ Symbiosis Inst Technol Pune 412115 Maharashtra India Nantong Univ Sch Informat Sci & Technol Nantong 226019 Peoples R China Monash Univ Malaysia Sch Informat Technol Bandar Sunway 47500 Subang Jaya Malaysia Univ Malaya Fac Engn Dept Elect Engn Kuala Lumpur Malaysia Symbiosis Int Symbiosis Inst Technol Symbiosis Ctr Appl Artificial Intelligence Pune 412115 India

As one of the most common female cancers, cervical cancer often develops years after a prolonged and reversible pre-cancerous stage. Traditional classification algorithms used for detection of cervical cancer often require cell segmentation and feature extraction techniques, while convolutional neural network (CNN) models demand a large dataset to mitigate over-fitting and poor generalization problems. To this end, this study aims to develop deep learning models for automated cervical cancer detection that do not rely on segmentation methods or custom features. Due to limited data availability, transfer learning was employed with pre-trained CNN models to directly operate on Pap smear images for a seven-class classification task. Thorough evaluation and comparison of 13 pre-trained deep CNN models were performed using the publicly available Herlev dataset and the Keras package in Google Collaboratory. In terms of accuracy and performance, DenseNet-201 is the best-performing model. The pre-trained CNN models studied in this paper produced good experimental results and required little computing time.

关键词： Cervical cancer classification Cervical cancer detection Pap smear images Convolutional neural network deep learning Medical image processing

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QUANTUM ADVERSARIAL learning FOR HYPERSPECTRAL REMOTE SENSING

QUANTUM ADVERSARIAL LEARNING FOR HYPERSPECTRAL REMOTE SENSIN...

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IEEE International Geoscience and Remote Sensing Symposium (IGARSS)

作者： Lin, Chia-Hsiang Kuo, Chen-Yu Young, Si-Sheng Natl Cheng Kung Univ Dept Elect Engn 1 Univ Rd Tainan 1 Taiwan Natl Cheng Kung Univ Inst Comp & Commun Engn 1 Univ Rd Tainan 1 Taiwan

ISBN: (纸本)9798350360332;9798350360325

Adversarial learning is of paramount importance in numerous recent computing areas, while quantum entanglement has also revealed its role in efficient computing. This motivated us to bring quantum computing into adversarial learning. Though there are some preliminary theoretical studies of quantum adversarial learning (QAL), there has no demonstration of advanced real-world applications. For the first time, we design and implement a QAL framework, and demonstrate the feasibility of QAL-based hyperspectral image processing for remote sensing. This research line aims to lay the foundation for the QAL in hyperspectral remote sensing, including to solve the limited qubit resources, to mitigate the quantum collapse effect, to design a quantum discriminator with comparable ability, as well as to ensure the quantum expressibility of the generator, to name a few.

关键词： Quantum adversarial learning quantum generative adversarial networks quantum image processing hyperspectral remote sensing quantum expressibility

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deep learning framework for interpretable quality control of echocardiography video

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MEDICAL PHYSICS 2025年第6期52卷 4039-4053页

作者： Du, Liwei Xue, Wufeng Qi, Zhanru Shi, Zhongqing Guo, Guanjun Yang, Xin Ni, Dong Yao, Jing Shenzhen Univ Med Sch Sch Biomed Engn Shenzhen Peoples R China Nanjing Univ Affiliated Hosp Dept Ultrasound Med Med Sch Nanjing Peoples R China Nanjing Univ Affiliated Hosp Med Imaging Ctr Med Sch Nanjing Peoples R China Yizheng Hosp Nanjing Drum Tower Hosp Grp Yangzhou Peoples R China

Background: Echocardiography (echo) has become an indispensable tool in modern cardiology, offering real-time imaging that helps clinicians evaluate heart function and identify abnormalities. Despite these advantages, the acquisition of high-quality echo is time-consuming, labor-intensive, and highly subjective. Purpose: The objective of this study is to introduce a comprehensive system for the automated quality control (QC) of echo videos. This system focuses on real-time monitoring of key imaging parameters, reducing the variability associated with manual QC processes. Methods: Our multitask network analyzes cardiac cycle integrity, anatomical structures (AS), depth, cardiac axis angle (CAA), and gain. The network consists of a shared convolutional neural network (CNN) backbone for spatial feature extraction, along with three additional modules: (1) a bidirectional long short-term memory (Bi-LSTM) phase analysis (PA) module for detecting cardiac cycles and QC targets;(2) an oriented object detection head for AS analysis and depth/CAA quantification;and (3) a classification head for gain analysis. The model was trained and tested on a dataset of 1331 echo videos. Through model inference, a comprehensive score is generated, offering easily interpretable insights. Results: The model achieved a mean average precision of 0.962 for AS detection, with PA yielding average frame errors of 1.603 +/-+/- 1.181 (end-diastolic) and 1.681 +/-+/- 1.332 (end-systolic). The gain classification model demonstrated robust performance (Area Under the Curve > 0.98), and the overall processing speed reached 112.4 frames per second. On 203 randomly collected echo videos, the model achieved a kappa coefficient of 0.79 for rating consistency compared to expert evaluations CONCLUSIONS: Given the model's performance on the clinical dataset and its consistency with expert evaluations, our results indicate that the model not only delivers real-time, interpretable quality scores but also demonst

关键词： echocardiography video multitask network quality control real-time visualized explanation

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Enabling Online Reinforcement learning Training for Open RAN 23

Enabling Online Reinforcement Learning Training for Open RAN

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23rd International-Federation-for-Information-processing (IFIP) Networking Conference (IFIP Networking)

作者： Lacava, Andrea Pietrosanti, Tommaso Polese, Michele Cuomo, Francesca Melodia, Tommaso Northeastern Univ Inst Wireless Internet Things Boston MA 02115 USA Sapienza Univ Rome Rome Italy

ISBN: (纸本)9798350390605;9783903176638

The Open Radio Access Network (RAN) architecture has introduced new elements in the RAN, i.e., the RAN Intelligent Controllers (RICs), which allow for closed-loop control of the physical infrastructure through custom, data-driven, intelligent applications. At the near-real-time RIC, xApps access RAN nodes through the E2 interface and offer the option to host traditional algorithms or data-driven deep Reinforcement learning (DRL) agents to control and optimize RAN functionalities. The O-RAN specifications suggest that Artificial Intelligence (AI) model training should not be done on production RAN deployments to avoid network disruptions and degradation in the Quality of Experience (QoE) of the User Equipments (UEs), suggesting the adoption of offline reinforcement learning. However, this approach limits the exploration phase during training to the static data that has already been collected, potentially affecting the performance of the model and its generalization capabilities. Therefore, a safe environment capable of supporting online reinforcement learning is needed to overcome such constraints and to allow AI agents to perform state explorations freely. In this paper, we present a new control environment based on Gymnasium (gym), a Python library for the creation of reinforcement learning environments, and ns-O-RAN, a software integration between a real-world near-real-time RIC and an Network Simulator 3 (ns-3) simulated RAN, which exposes the RAN Key Performance Indicators (KPIs) through a standardized Application Programming Interface (API) ready to be used by any solving approach. Leveraging ns-O-RAN, we create an environment that dynamically captures the simulated O-RAN telemetry, waits for the agent to compute a decision, receives and delivers such control action to update the RAN configuration in the underlying simulation, allowing the development and test of models in safe and reproducible conditions. Finally, our framework exposes an abstract API interfac

关键词： Open RAN ns-3 deep reinforcement learning artificial intelligence gymnasium

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An IoT-based intelligent farming using CNN for early disease detection in rice paddy

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MICROPROCESSORS AND MICROSYSTEMS 2022年 94卷

作者： Debnath, Oliva Saha, Himadri Nath Indian Inst Technol Dept Comp Sci Engn Hyderabad India Calcutta Univ Surendranath Evening Coll Dept Comp Sci Kolkata West Bengal India

Huge economic losses occur in the agricultural industry due to bacterial, viral or fungal infections in crops due to which farmers incur 15-20% losses on their profits annually. India is the second largest producer of rice and a leading exporter of the same in the global market. Thus, early disease detection in crops is essential. Implementing Smart Farming is a burning area of research in order to prevent further damage to crops. The widespread development of deep learning makes it possible to achieve the goal of disease detection in crops. In this paper we have proposed an intelligent model based on Smart farming integrating Machine learning with the IoT network. The novelty of this project is early detection of Brown spot disease in rice paddy for the very first time using Convolutional Neural Networks. deep learning uses Neural Networks to implement Artificial Intelligence. This project makes use of image recognition and pre-processing based on real time data. Data preprocessing and feature extraction has been done using a self-designed image-processing tool. Tensor flow and Keras framework has been implemented on both training and testing data collected manually from rice fields. The proposed model achieved an accuracy of 97.701% posing the ability to minimise the losses overall to the national and global productions. Further an app has been designed for the farmers for the same.

关键词： Artificial intelligence deep learning deep neural networks Convolutional neural networks Big data Early detection of disease Smart farming Internet of Things Rice paddy

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Tasneef: A Fast and Effective Hybrid Representation Approach for Arabic Text Classification

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IEEE ACCESS 2024年 12卷 120804-120826页

作者： Louail, Maroua Hamdi-Cherif, Chafia Kara-Mohamed Hamdi-Cherif, Aboubekeur Ferhat Abbas Univ Setif 1 Comp Sci Dept LRSD Lab Setif 19000 Algeria Ferhat Abbas Univ Setif 1 Comp Sci Dept Setif 19000 Algeria

The Arabic language role in actual global affairs entails sophisticated natural language processing techniques, especially in text classification. This paper presents Tasneef as a novel hybrid approach to tackle computational challenges by reducing memory usage and runtime overhead for actual Arabic text classification (ATC). Tasneef integrates distance-based meta-features (DBMFs) representation with word embeddings. This integration is useful because using a single text representation technique can be limiting in capturing the essential range of features necessary for effective classification, especially in complex languages like Arabic. By addressing the intricacies arising from the high dimensionality and sparsity inherent in Term Frequency-Inverse Document Frequency (TF-IDF) representation, the utilization of DBMFs is shown to offer a promising solution. The DBMFs rely on document labels and statistical features to establish meaningful distance relationships between documents, thereby facilitating effective reduction. Furthermore, word embeddings encapsulate semantic attributes. Empirical assessments reveal a significant reduction of two orders of magnitude in both memory usage and runtime. This reduction translates to memory savings ranging from 158x to 361x and runtime reductions from 120x to 524x across three popular datasets;maintaining comparable MicroF1 and MacroF1 values, while notably reducing learning time. Moreover, Tasneef outperforms ten state-of-the-art deep learning models and seven dimension reduction methods in accuracy, with enhancements ranging from 0.3% to 39.6%;and F-Measure, with improvements from 4.6% to 26.8%, across four additional datasets. These findings highlight Tasneef as a promising solution for diverse ATC applications in real-world scenarios, offering concise and rapid classification with reduced computational learning costs.

关键词： Text categorization Accuracy Runtime Linguistics Semantics Feature extraction Natural language processing Text processing Arabic natural language processing Arabic text classification Arabic text representation feature extraction meta-features word embeddings

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IntelligentDine: Advanced Food Identification and Caloric Assessment through deep learning for Smart Diet Tracking 2

IntelligentDine: Advanced Food Identification and Caloric As...

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2nd IEEE International Conference on Trends in Quantum Computing and Emerging Business Technologies, TQCEBT 2024

作者： Vaithiyanathan, M. Prasad, Ch. Krishna Lenka, Abhipsa Nayak, Sasmita Maniraj, P. Arigela, Arun Kumar Department of Information Technology Sri Sairam Engineering College Chennai India Department of Computer Science and Engineering Anurag Engineering College Telangana Kodad India Department of Electronics and Instrumentation Engineering Odisha University of Technology and Research Odisha Bhubaneswar India Department of Mechanical Engineering Government College of Engineering Kalahandi OdishaBhawani Patna India Department of Electrical and Electronics Engineering M.Kumarasamy College of Engineering Karur Thalavapalayam India Telangana Hyderabad India

ISBN: (纸本)9798350384277

The IntelligentDine initiative introduces an inventive approach to enhance diet tracking and promote healthy eating habits by applying advanced deep learning techniques. The primary goal is to precisely identify various food items and offer real-time estimates of their calorie content, providing users with intelligent and personalized diet monitoring capabilities. Using the Python programming language, we employed the ResNet150 architecture model for food recognition and calorie estimation, utilizing the extensive Food 101 dataset with 37,046 food images spanning 101 distinct food classes. Following rigorous training and evaluation, our model demonstrated notable success, achieving a training accuracy of 97.30% and a validation accuracy of 98.02%. These compelling results underscore the efficacy of our proposed approach in accurately recognizing and categorizing diverse food items. Smart Bite, a key component of this initiative, harnesses deep learning algorithms to swiftly analyze input images within a web framework, identifying specific food items and estimating their calorie content. This valuable information empowers users to effectively monitor their dietary *** intelligent diet monitoring capabilities of Smart Bite enable users to make informed decisions about their food choices. By tracking and analyzing daily food intake, users can gain insights into their nutritional habits, set personalized goals, and make necessary adjustments for a balanced and healthy diet. SmartBite showcases the successful integration of deep learning techniques, specifically employing the ResNet150 architecture for food recognition and calorie estimation. With its impressive accuracy, real-time processing capabilities, and intelligent diet monitoring features, SmartBite has the potential to transform how individuals track and manage their dietary habits, fostering healthier lifestyles. © 2024 IEEE.

关键词： Caloric deep learning Analysis food images real-time ResNet150

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2024 6th International Conference on Natural Language processing, ICNLP 2024

2024 6th International Conference on Natural Language Proces...

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6th International Conference on Natural Language processing, ICNLP 2024

ISBN: (纸本)9798350349115

The proceedings contain 135 papers. The topics discussed include: a review of deep learning based image steganography methods;lightweight underwater target detection algorithm based on YOLOv7;research and design of low power sigma-delta modulator based on double sampling;health state recognition of bearing based on time-frequency spectrogram and deep learning;enhancing multilingual natural language processing with custom subword tokenization: Subword2Vec and BiLSTM integration for lightweight and streamlined approaches;task offloading and resource allocation strategies based on proximal policy optimization;linguistic-aware WordPiece tokenization: semantic enrichment and OOV mitigation;sparse transformer visual tracking network based on second-order attention;and abnormal activity detection using video action recognition: a review.

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