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Single-Stage Deep Learning in Aviation: YOLOv5 for Satellite-Based Aircraft Detection 8

Single-Stage Deep Learning in Aviation: YOLOv5 for Satellite...

引用

8th International Artificial Intelligence and Data Processing symposium, IDAP 2024

作者： Duhter, Melis Schultheiss, Klaudia Marek, Hana Marie Starha, Monika Lenka Kuzu, Hilal Mersin University Department of Computer Engineering Mersin Turkey University of Bremen Department of Engineering Technology Bremen Germany Technical University of Liberec Department of Applied Mechanics Liberec Czech Republic Tarsus University Aerospace Engineering Department Mersin Turkey

ISBN: (纸本)9798331531492

Recent advancements in object detection technology have revolutionized real-time remote sensing capabilities through satellite imagery. Close-orbit satellites, unmanned aerial vehicles, and autonomous vehicles have emerged as preferred platforms for object detection, offering operational advantages across various applications. Deep learning algorithms play a pivotal role in object detection. Aircraft detection is particularly critical in civil and military aviation contexts, facilitating efficient traffic routing at airports and enhancing national defense strategies. However, satellite-based remote sensing faces challenges such as image resolution limitations, environmental influences, and complexities in distinguishing small, densely packed aircraft in datasets. To address these challenges, deep learning techniques harness neural networks optimized for parallel processing on GPUs, significantly improving object detection accuracy and efficiency. This study focuses on evaluating the YOLOv5 detection algorithm for ground aircraft detection using satellite-based aerial imaging. YOLOv5's single-stage architecture enables real-time detection and classification of objects with enhanced accuracy and efficiency, making it ideal for applications requiring rapid analysis of large-scale aerial images. Leveraging advanced deep learning methodologies, this research assesses YOLOv5's performance in aircraft detection, highlighting its potential in advancing aviation management, surveillance, and security practices. © 2024 ieee.

关键词： Remote sensing

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real-time embedded Object Tracking with Discriminative Correlation Filters Using Convolutional Features 1

引用

18th International symposium on Applied Reconfigurable Computing

作者： Danilowicz, Michal Kryjak, Tomasz AGH Univ Sci & Technol Dept Automat Control & Robot Comp Vis Lab Embedded Vis Syst Grp Krakow Poland

ISBN: (数字)9783031199837

ISBN: (纸本)9783031199820;9783031199837

Object tracking is an essential element of visual perception systems. It is used in advanced video surveillance systems (AVSS), autonomous vehicles, robotics, and many more. For applications such as autonomous robots, the system must be implemented on some embedded platform with limited computing performance and power. Furthermore, sufficiently fast response is required from the tracking system in order to perform some real-time tasks. Discriminative Correlation Filter (DCF) based tracking algorithms are popular for such applications, as they offer state-of-the-art performance while not being too computationally complex. In this paper, an FPGA implementation of the DCF tracking algorithm using convolutional features is presented. The ZCU104 board is used as a platform, and the performance is evaluated on the VOT2015 dataset. In contrast to other implementations that use HOG (Histogram of Oriented Gradients) features, this implementation achieves better results for 64 x 64 filter size while being able to potentially operate at higher speeds (over 467 fps per scale).

关键词： Discriminative correlation filter Object tracking FPGA real-time image processing

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Active Control for Resonance Mitigation in Electric Drive applications 15

Active Control for Resonance Mitigation in Electric Drive Ap...

引用

15th International symposium on Industrial Electronics and applications, INDEL 2024

作者： Berglund, Emelie Borg, Johan Johansson, Jonny Luleå University of Technology Dept. of Computer Science Electrical and Space Engineering Sweden

ISBN: (纸本)9798350352320

The use of inverter-based motor controllers that can generate fast voltage level transitions is increasing in the field of electric drive systems because of their ability to achieve higher efficiency. However, the fast voltage transitions, dv/dt's, are prone to generate bearing currents and cause overvoltages that may harm the electric motor. An LC filter applied between the inverter and the motor terminals can reduce the dv/dt's. However, the LC filter will cause overvoltage due to resonance but this can be overcome by introducing an extra pulse at each pulse-width modulation (PWM) transition. The timing of this extra pulse is crucial for suppressing the resonance, but due to nonlinearities in a real system, the time instants may vary over time and are thus in need of control. In this study, an analytical expression for the optimal pulse location and duration is derived and we propose a control algorithm that continuously calculates the optimal pulse switch time instants based on the measured output voltage. The control algorithm can handle sudden system disturbances and a Monte Carlo simulation is performed to evaluate the performance of the algorithm. This hybrid filter successfully reduces dv/dt's at the motor terminals while simultaneously eliminating overvoltages due to the filter resonance. © 2024 ieee.

关键词： Control nonlinearities

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An embedded SHM System for Monitoring of Pipelines Using Torsional Guided Wave Ultrasonics

An Embedded SHM System for Monitoring of Pipelines Using Tor...

引用

2023 Joint Conference of the European Frequency and time Forum and ieee International Frequency Control symposium, EFTF/IFCS 2023

作者： Patil, Sheetal Mahajan, Sagar Banerjee, Sauvik Tallur, Siddharth IIT Bombay Department of Electrical Engineering Mumbai India IIT Bombay Department of Civil Engineering Mumbai India

ISBN: (纸本)9798350311426

Ultrasonic guided wave (GW)-based structural health monitoring (SHM) is highly effective for real-time monitoring of emerging defects in infrastructure, including pipelines. Torsional GW modes are of great interest in pipeline monitoring, being non-dispersive and not attenuated by presence of fluids inside pressurized pipes. In this work, we leverage torsional GW modes for monitoring of a metallic pipe using in-plane poled thickness-shear d15 PZT transducers and a custom-designed FPGA-based embedded system for transduction and signal processing. The sensor configuration used in this work is suitable for transduction of the axisymmetric torsional mode, confirmed through finite element (FE) simulations and experiments. We also study the interaction of the axisymmetric torsional mode with circumferential defects (crack and notch). © 2023 ieee.

关键词： Guided wave ultrasonics pipeline structural health monitoring torsional mode

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Deep Learning-Based Fruit and Vegetable Detection 9

Deep Learning-Based Fruit and Vegetable Detection

引用

9th International Conference on Information technology and Digital applications, ICITDA 2024

作者： Kang, Ho Jun Yogarayan, Sumendra Mogan, Jashila Nair Min, Pa Pa Faculty of Information Science and Technology Melaka Malaysia

ISBN: (纸本)9798331510732

In recent times, suppliers in the agricultural sector are under increasing pressure to enhance efficiency in sorting and categorizing fruits and vegetables. Ensuring accurate and efficient detection is crucial for optimizing supply chain operations. This study presents the development of an embedded system using deep learning techniques to address these challenges. Specifically, a Convolutional Neural Network (CNN) model is implemented on a Raspberry Pi 5, designed to improve the accuracy of fruit and vegetable identification for suppliers. The system integrates image capture and real-time detection capabilities, streamlining the sorting process. The dataset, which includes images of various produce, undergoes preprocessing steps such as grayscale conversion and feature scaling. A train-test split is employed, with 60% of the data allocated for training, 20% for validation, and 20% for testing. The CNN model is trained and evaluated, demonstrating high classification accuracy. © 2024 ieee.

关键词： Fruits

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Comparative Study on real time Image Data Transmission Methods for Unmanned Aerial Vehicle

Comparative Study on Real Time Image Data Transmission Metho...

引用

ieee International symposium on Geoscience and Remote Sensing (IGARSS)

作者： Chenglin Zhang Xiaoyong Qiang Qingzhe Lv Hao Guan Fang Huang School of Resources and Environment University of Electronic Science and Technology of China Chengdu P.R. China

ISBN: (数字)9798350360325

ISBN: (纸本)9798350360332

With the development of unmanned aerial vehicles (UAV) and embedded development technology, more and more UAV remote sensing applications require real-time transmission of acquired image data or processed result data to ground workstations. In this case, the method, speed, and quality of data transmission will have a significant impact on the real-time performance of such UAV applications. In response to this issue, this study is based on an UAV ground target real-time recognition system, and discusses data transmission methods based on wireless local area network, Internet of Things technology and TCP/IP communication protocol. It explores data transmission methods based on 4G communication network internal network penetration and Cloud server combination, and conducts comparative experimental analysis on these methods. Finally, through experiments, it was found that the combination of 4G communication network and Cloud server is a better method for transmission performance. This method avoids the spatial distance limitations of traditional methods and achieves reliable and superior real-time image data transmission.

关键词： Cloud computing Wireless LAN Target recognition Autonomous aerial vehicles real-time systems Workstations Data communication

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Smart Helmet And Monitoring For Miners With Enhanced Protection 2

Smart Helmet And Monitoring For Miners With Enhanced Protect...

引用

2nd International Conference on Edge Computing and applications, ICECAA 2023

作者： Banik, Arnob Mishra, Divakar Manikandan, N. School of Computer Science and Engineering Vellore Institute of Technology Tamil Nadu Vellore India School of Computer Science and Engineering Vellore Institute of Technology Department of Database Systems Tamil Nadu Vellore India

ISBN: (纸本)9798350347579

Internet of Things (IoT) has lately been expanded across various applications, drawing significant attention to its design, where a loud industrial atmosphere can exist in the mining area. The central essence of this project was to detect hazardous events and to design a perfect, smart helmet that ensures the safety of workers. We have decided to implement a system that could be embedded in the helmets of the mine workers. Workers would be alerted via a helmet buzzer, so necessary action was taken on time. The critical feature of this project was that the model presented uses an application interface to detect real-time data and display it. The administrators outside may monitor the dynamic data from the system using a website and a mobile application. In contrast, the Android app displays or informs the data or alerts. The miners and the administrators can monitor real-time data that constantly changes according to the changes in the environment. They could also monitor the changes in temperature and humidity through graphs continuously updated on the applications. 'Rescue with Flexibility' was the main motto of our project. Thus, this project offers a directional platform that would bring a revolutionary change in this industry. © 2023 ieee.

关键词： Internet of things

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Intelligent Food Identification System Using Improved YOLOv8 13

Intelligent Food Identification System Using Improved YOLOv8

引用

13th International symposium on Computer Science and Educational technology, ISCSET 2024

作者： Nitiavintokana, Francia Rouf, Md Abdur Yu, Xiaoyu Wu, Haibin Wang, Aili Iwahori, Yuji Harbin University of Science and Technology Heilongjiang Province Key Laboratory of Laser Spectroscopy Technology and Application Harbin China College of Electron and Information University of Electronic Science and Technology of China Zhongshan Institute Zhongshan China Chubu University Department of Computer Science Aichi Japan

ISBN: (纸本)9798350390803

The field of computer vision has witnessed significant advances in object detection with real-world applications ranging from autonomous driving to surveillance systems. Due to its importance in a number of areas, such as dietary evaluation, food industry automation, and health monitoring, food identification has attracted a lot of interest in this context. The suggested approach makes use of the capabilities of two cutting-edge object recognition models, YOLOv8. Because of its speed and accuracy, YOLOv8 is a great option for real-time food detection in a variety of settings. Our most recent YOLOv8 model is called BiTrains-YOLOv8. Supervision techniques are applied to improve the combined model's performance even more. This increases overall detection accuracy and helps the algorithm to more effectively generalize to novel food items. Based on the detection results, 96.7% of an average of detection accuracy, 96.4% for precision, 95.7% for recall, and 96.04 % for F1-Score were obtained. The results show that the combination of YOLOv8 and BiTrains-YOLOv8 improved model techniques, significantly improved the performance of food detection, outperforming the previous methods in terms of accuracy, speed and robustness. Furthermore, the paper analyzes potential real-world applications of the system, such as calorie counting, dietary assessment and smart kitchen automation. Implications of advanced intelligent food detection in healthcare, the culinary industry and personalized nutrition are also discussed. In conclusion, the results provide valuable insights for pushing the boundaries of food recognition technology, with promising applications in various fields that promote healthy lifestyles and improve efficiency in food-related processes. © 2024 ieee.

关键词： Food industry

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Enhanced real-time Mechanical Measurement Monitoring Through Cross- Platform BLE Interface in Windows and Android applications 3

Enhanced Real-Time Mechanical Measurement Monitoring Through...

引用

3rd International Conference on Sentiment Analysis and Deep Learning, ICSADL 2024

作者： Vandana, N. Preethi, D. Kusuma, N. Keshava Murthy, G.N. Siddaganga Institute of Technology VlSI Design and Embedded System India CMTI India India Siddaganga Institute of Technology Electronic and Instrumentation Engg India

ISBN: (纸本)9798350396157

There are various mechanical measurement techniques used in metrology applications. Measurement data can be acquired from measuring device / instruments like probes, gauges, calibration instruments, using various communication protocols Communication ports (COM Port), Universal Serial Communication ports (USBs), Blue tooth, Ethernet, and many more. In this paper, an attempt has been made to adapt Bluetooth Low Energy (BLE), a high end Bluetooth technique to acquire and monitor the measurement data from measuring device/ instrument using Visual Studio C++ platform (with GUI development in QT framework). The detailed method/procedure of data acquisition using BLE technique is clearly described in this paper which is very useful for the development of measurement software using BLE. © 2024 ieee.

关键词： Bluetooth

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Webcam-Guided Gesture-Controlled Robotic Arm 3

Webcam-Guided Gesture-Controlled Robotic Arm

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3rd ieee World Conference on Applied Intelligence and Computing, AIC 2024

作者： Singh, Kanishk Joshi, Aditya Bhatt, Jaishankar Juyal, Amit Rawat, Manmohan Singh Graphic Era Deemed to Be University Department of Computer Applications Dehradun India Graphic Era Deemed to Be University Department of Computer Science and Engineering Dehradun India Graphic Era Hill University School of Computing Dehradun India Uttarakhand State Council for Science and Technology Dehradun India

ISBN: (纸本)9798350384598

This paper focuses on the design of a gesture-controlled robotic arm that is navigated with the help of a webcam and OpenCV-enabled real-time hand tracking. embedded with OpenCV's hand tracking module, the system successfully identifies the hand landmarks from the live camera captured hand movement of the user. By the current state of each finger, the state is represented by a string format that includes the symbol and number (such as00010) that is transmitted to an Arduino Uno board, the robotic arm can move its fingers up or down. Control of the flows of the movement of the robotic arm is done using the Arduino Uno whereby the servo motors receive signals to move 0 or 180 degrees. This is mainly due to the efficiency, precision and short latency of the suggested system which makes it novel that gives the user a simple graphics interface to control robotic arms without necessity to know programming or have specific hardware. This technology provides an effective solution in robotic manipulation and presents the enormous potential to enhance HRI in different application areas. © 2024 ieee.

关键词： Robotic arms

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