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Enhancing Object detection in Assistive Technology for the Visually Impaired: A DETR-Based Approach

IEEE ACCESS 2025年 13卷 71647-71661页

作者： Ikram, Sunnia Sarwar Bajwa, Imran Gyawali, Sujan Ikram, Amna Alsubaie, Najah Islamia Univ Bahawalpur Dept Comp Sci Bahawalpur 63100 Pakistan Lamar Univ Dept Comp Sci Beaumont TX 77705 USA Govt Sadiq Coll Women Univ Dept Comp Sci Bahawalpur 63100 Pakistan Princess Nourah bint Abdulrahman Univ PNU Coll Comp & Informat Sci Dept Comp Sci POB 84428 Riyadh 11671 Saudi Arabia

This paper presents a real-time obstacle detection and recognition system designed to enhance navigation for visually impaired individuals through assistive technology. The system integrates a mobile application equipped with a mini camera for real-time image capture and employs advanced deep learning techniques for object detection and classification. A comparative evaluation of YOLOv8, Faster R-CNN and DETR (detection Transformer) is conducted based on precision, Recall, F1-score, confidence score and processing efficiency. DETR demonstrates superior performance, achieving a 99% confidence score, 98% precision and a processing speed of 40ms per frame. While faster R-CNN and YOLOv8 provide competitive results, they offer a trade-off between accuracy and computational efficiency. The system follows a structured a structured workflow, including real-time acquisition, preprocessing, innovative data augmentation and optimization for edge devices using TensorFlow Lite for efficiency deployment. It classifies 80 types of obstacles, such as pedestrians, vehicles and traffic signal and provides immediate audio feedback to ensure safe navigation. The model trained over 20 epochs achieves an accuracy of 98% in the final epoch. This study introduces a scalable and practical solution integrating IoT and real-time image processing, empowering visually impaired users with enhanced mobility and safety.

关键词： Sensors Navigation Accuracy Real-time systems Cameras Mobile applications Sensor systems Safety detection algorithms Computational modeling Computer vision detection transformer (DETR) region-based convolutional neural network (R-CNN) IoT-based object detection performance evaluation of deep learning models real-time object recognition You Only Look Once (YOLOv8)

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Spectral-Based Proactive Blockage detection for Sub-THz Communications

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IEEE COMMUNICATIONS LETTERS 2024年第7期28卷 1703-1707页

作者： Zhinuk, Fariha Gaydamaka, Anna Moltchanov, Dmitri Koucheryavy, Yevgeni Tampere Univ Fac Informat Technol & Commun Sci Tampere 33100 Finland

Human body blockage is one of the main reasons prohibiting the adoption of modern millimeter wave (mmWave, 30-70 GHz) and future sub-terahertz (sub-THz, 100-300 GHz) cellular systems. To utilize system-level blockage avoidance solutions, such as multiconnectivity, one must be able to detect blockage events prior to their occurrence. To this end, recent approaches have utilized machine learning algorithms operating in the time domain. In our letter, we advocate for the use of spectral representation, where the gap between pre-blockage and non-blockage periods is much wider and directly measurable. By utilizing this idea, we developed a simple proactive blockage detection algorithm for indoor deployment of sub-THz systems and evaluated it using blockage detection probability, mean time to blockage, and false alarm rate. Our results obtained using measured traces of blockage events at 156 GHz show that it is capable of detecting a blockage event at least 50 ms prior to its occurrence. Compared to other approaches, it is characterized by a false alarm rate of less than one events/s making it robust even in fading environments.

关键词： Time-domain analysis Smoothing methods Millimeter wave communication detection algorithms Oscillators Loss measurement Fourier transforms 5G 6G millimeter wave terahertz proactive blockage detection blockage signatures

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Mitosis detection on histopathological images using statistical detection algorithms

Mitosis detection on histopathological images using statisti...

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IEEE Signal Processing and Communications Applications (SIU)

作者： Mustafa Üstüner Gökhan Bilgin Harita Mühendisliği Bölümü İstanbul Türkiye Bilgisayar Mühendisliği Bölümü Yıldız Teknik Üniversitesi İstanbul Türkiye

In this work, the utility and accuracy of the statistical detection algorithms for the detection of mitosis on histopathological images have been investigated. In the first stage, the subset images involving mitotic cells from the original images have been created. The occurance based texture filters have been applied to each subset image. Then the training/testing dataset has been created from these subset images. Later, the three statistical detection algorithms have been implemented in this work, namely matched filtering (MF), constrained energy minimization (CEM) and adaptive coherence estimator (ACE). The accuracies over 80% have been obtained for each method and four different evaluation measures have been utilized. The results indicate that the MF is the best algorithm on mitosis detection among the implemented algorithms.

关键词： Hyperspectral imaging Filtering Coherence detection algorithms Minimization

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Evaluating Real-Time Anomaly detection algorithms -- The Numenta Anomaly Benchmark

Evaluating Real-Time Anomaly Detection Algorithms -- The Num...

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International Conference on Machine Learning and Applications (ICMLA)

作者： Alexander Lavin Subutai Ahmad Numenta Inc. Redwood City CA

Much of the world's data is streaming, time-series data, where anomalies give significant information in critical situations, examples abound in domains such as finance, IT, security, medical, and energy. Yet detecting anomalies in streaming data is a difficult task, requiring detectors to process data in real-time, not batches, and learn while simultaneously making predictions. There are no benchmarks to adequately test and score the efficacy of real-time anomaly detectors. Here we propose the Numenta Anomaly Benchmark (NAB), which attempts to provide a controlled and repeatable environment of open-source tools to test and measure anomaly detection algorithms on streaming data. The perfect detector would detect all anomalies as soon as possible, trigger no false alarms, work with real-world time-series data across a variety of domains, and automatically adapt to changing statistics. Rewarding these characteristics is formalized in NAB, using a scoring algorithm designed for streaming data. NAB evaluates detectors on a benchmark dataset with labeled, real-world time-series data. We present these components, and give results and analyses for several open source, commercially-used algorithms. The goal for NAB is to provide a standard, open source framework with which the research community can compare and evaluate different algorithms for detecting anomalies in streaming data.

关键词： Detectors Real-time systems Benchmark testing Measurement detection algorithms Standards Algorithm design and analysis

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Understanding Deep MIMO detection

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IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS 2023年第12期22卷 9626-9639页

作者： Hu, Qiang Gao, Feifei Zhang, Hao Li, Geoffrey Ye Xu, Zongben Tsinghua Univ Dept Elect Engn Beijing 100084 Peoples R China Tsinghua Univ Inst Artificial Intelligence Beijing Natl Res Ctr Informat Sci & Technol BNRist State Key Lab Intelligent Technol & Syst Beijing 100084 Peoples R China Tsinghua Univ Dept Automat Beijing 100084 Peoples R China Imperial Coll London Dept Elect & Comp Engn South Kensington Campus London SW7 2AZ England Xi An Jiao Tong Univ Sch Math & Stat Xian 710049 Peoples R China

Incorporating deep learning (DL) into multiple-input multiple-output (MIMO) detection has been deemed as a promising technique for future wireless communications. However, most of the DL-based MIMO detection algorithms are lack of interpretation on internal mechanisms. In this paper, we analyze the performance of the DL-based MIMO detection to better understand its strengths and weaknesses. We investigate and compare two different models: data-driven DL detector with neural networks activated by rectifier linear unit (ReLU) function and model-driven DL detector based on traditional detection algorithms. We show that the data-driven DL detector asymptotically approaches to the maximum a posterior (MAP) detector in various scenarios but requires a large amount of training samples to converge in time-varying channels. On the other hand, the model-driven DL detector utilizes the expert knowledge to alleviate the impact of channels and achieves relatively high detection accuracy with a small set of training data. Simulation results confirm our analytical results and demonstrate the effectiveness of the DL-based MIMO detection for both linear and nonlinear signal systems.

关键词： Detectors MIMO communication detection algorithms Wireless communication Symbols Training Iterative algorithms Explainable deep learning MIMO detection deep neural network model-driven

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On Performance Evaluation of Driver Hand detection algorithms: Challenges, Dataset, and Metrics

On Performance Evaluation of Driver Hand Detection Algorithm...

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International IEEE Conference on Intelligent Transportation Systems

作者： Nikhil Das Eshed Ohn-Bar Mohan M. Trivedi Laboratory of Intelligent and Safe Automobiles University of California San Diego La Jolla California 92093

ISBN: (纸本)9781467365970

Hands are used by drivers to perform primary and secondary tasks in the car. Hence, the study of driver hands has several potential applications, from studying driver behavior and alertness analysis to infotainment and human-machine interaction features. The problem is also relevant to other domains of robotics and engineering which involve cooperation with humans. In order to study this challenging computer vision and machine learning task, our paper introduces an extensive, public, naturalistic video-based hand detection dataset in the automotive environment. The dataset highlights the challenges that may be observed in naturalistic driving settings, from different background complexities, illumination settings, users, and viewpoints. In each frame, hand bounding boxes are provided, as well as left/right, driver/passenger, and number of hands on the wheel annotations. Comparison with an existing hand detection datasets highlights the novel characteristics of the proposed dataset.

关键词： driver hands problem challenges Hand Dataset Performance Evaluation Drivers Driver Behavior detection algorithms images computer vision robotics driver circuits

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Deep Learning algorithms in Aircraft detection and Classification: An Analytical Survey

Deep Learning Algorithms in Aircraft Detection and Classific...

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International Conference on Developments in eSystems Engineering (DESE)

作者： Noora Alkharji Hesssa Almazrouei Shaikha Alzaabi Ali Bou Nassif Mariame Elsalhy Manar Abu Talib Department of Computer Engineering University of Sharjah Sharjah United Arab Emirates Department of Computer Science University of Sharjah Sharjah United Arab Emirates

ISBN: (数字)9798350368697

ISBN: (纸本)9798350368703

Aircraft detection has become a very crucial aspect in the aviation industry due to its vital role in identifying unauthorized aircraft trespassing into restricted areas. Due to their significant role in decision-making, these detection systems must have a high level of accuracy. In this paper, we address deep learning feature extraction and aircraft detection methods. This study aims to explore deep learning-based feature extraction techniques and airplane identification techniques. The applications examined stop unauthorized aircraft from entering restricted airspace by utilizing a range of deep learning models. The study results and basic information on feature types, image types, and detection techniques are presented in this paper.

关键词： Deep learning Measurement Surveys Industries Accuracy Feature detection Decision making Feature extraction Aircraft detection algorithms

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Robust template based corner detection algorithms for robotic vision

Robust template based corner detection algorithms for roboti...

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IEEE International Conference on Technologies for Practical Robot Applications (TePRA)

作者： Chen Gao Karen Panetta Sos Agaian Electrical and Computer Engineering Department Tufts University Medford MA USA Electrical and Computer Engineering Department University of Texas at San Antonio San Antonio TX USA

Image corners encapsulate gradient changes in multiple directions. Therefore, corners are considered as efficient features for use in robotic navigation algorithms. Template based corner detection has a low computational complexity and is straightforward to implement. With the appropriate design of templates, satisfactory detection accuracy can also be achieved. In this paper, we introduce two new template based corner detection algorithms to be used to assist robot vision: the matching based corner detection, namely, MBCD; and the correlation based corner detection, namely, CBCD. These two approaches outperform existing template based approaches in the means that they reduce detection of spurious corners by considering ideal corners with at least two-pixel length on the corner arm directions. Experimental results show that the proposed algorithms detect essential corners for synthetic images and natural images satisfactorily according to human visual perception. We also examine the robustness of the two corner detection approaches in terms of the average repeatability and localization error. Since our approaches are computationally efficient, it makes these template based corner detection algorithms suitable for real time support in robotic applications. Comparisons with existing corner detection algorithms are also presented.

关键词： detection algorithms Image edge detection Correlation Robustness Feature extraction Robot sensing systems

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Evaluating Driving Fatigue detection algorithms Using Eye Tracking Glasses

Evaluating Driving Fatigue Detection Algorithms Using Eye Tr...

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International IEEE/EMBS Conference on Neural Engineering

作者： Xiang-Yu Gao Yu-Fei Zhang Wei-Long Zheng Bao-Liang Lu Dept. of Comput. Sci. & Eng. Shanghai Jiao Tong Univ. Shanghai China

ISBN: (纸本)9781467363877

Fatigue is a status of human brain activities, and driving fatigue detection is a topic of great interest all over the world. In this paper, we propose a measure of fatigue produced by eye tracking glasses, and use it as the ground truth to evaluate driving fatigue detection algorithms. Particularly, PERCLOS, which is the percentage of eye closure over the pupil over time, was calculated from eyelid movement data provided by eye tracking glasses. Experiments of a vigilance task were carried out in which both EOG signals and eyelid movement were recorded. The evaluation results of an effective EOG-based fatigue detection algorithm convinced us that our proposed measure is an appropriate candidate for evaluating driving fatigue detection algorithms.

关键词： Fatigue Glass Feature extraction Tracking detection algorithms Electrooculography Eyelids

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DECONSTRUCTING OPTIMAL detection algorithms

DECONSTRUCTING OPTIMAL DETECTION ALGORITHMS

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Workshop on Hyperspectral Image and Signal Processing

作者： Alan Schaum US Naval Research Laboratory

Composite hypothesis testing problems admit a class of solutions that are as optimal as possible, absent a cost function. These indomitable detectors comprise a superset of all optimal solutions. However, evaluating them with finite precision arithmetic can generate unstable answers. Here we devise a general method for deriving indomitables, using an extended fusion technique. Then we show how to reverse the process to generate arbitrarily accurate approximations to indomitables that are also stable.

关键词： Detectors Light rail systems detection algorithms Testing Bayes methods Fuses Mathematical model

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