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real-time image processing and deep learning 2021

ISBN: (纸本)9781510643093

The proceedings contain 20 papers. The topics discussed include: a review of real-time human action recognition involving vision sensing;embedded real-time people detection and tracking with time-of-flight camera;real-time object detection in 360-degree videos;a real-time software framework for driver monitoring systems: software architecture and use cases;real time circle detection by simplified Hough transform on smartphones;the evaluation of CUDA performance on the Jetson NanoBoard for an image binarization task;chest X-ray classification using transfer learning on multi GPU;and parallel implementation of a hyperspectral feature extraction method based on Gabor filter.

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A hardware accelerator to support deep learning processor units in real-time image processing

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ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2025年 145卷

作者： Cittadini, Edoardo Marinoni, Mauro Buttazzo, Giorgio Scuola Super Sant Anna Pisa Italy

deep neural networks are becoming crucial in many cyber-physical systems involving complex perceptual tasks. For those embedded systems requiring real-time interactions with dynamic environments, as autonomous robots and drones, it is of paramount importance that such algorithms are efficiently executed onboard on properly designed hardware accelerators to meet the required performance specifications. In particular, some neural network architectures for object detection and tracking, as You Only Look Once (YOLO), include heavy computational stages that need to be executed before and after the model inference. Such stages are typically not incorporated in traditional accelerators and are executed on general-purpose processors, thus introducing a bottleneck in the overall processing pipeline. To overcome such a problem, this paper presents a generalpurpose accelerator on a field-programmable gate array (FPGA) able to run pre-processing and post-processing operations typically required by vision tasks. The proposed solution has been tested in combination with a YOLO object detector accelerated on an Advanced Micro Devices (AMD) Xilinx Kria KR260 board mounting an UltraScale+ multiprocessor system-on-chip, achieving a significant improvement in terms of both timing performance and power consumption, and enabling onboard visual processing into drones. The proposed solution is able to boost the traditional object detection process by a factor of 4.4, allowing the execution of the full processing pipeline at 60 frames per second (fps), versus 13.6 fps reachable without the proposed accelerator. Asa result, this work enables the use of high-speed cameras for developing more reactive systems that can respond to incoming events with lower latency.

关键词： Hardware acceleration Object detection deep neural networks real-time processing image pre-processing acceleration deep processing unit

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Performance Evaluation of YOLO-Based deep learning Models for real-time Armour Unit Detection with image Pre-processing Method 26

Performance Evaluation of YOLO-Based Deep Learning Models fo...

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26th International Electronics Symposium (IES) on Shaping the Future - Society 5.0 and Beyond

作者： Pratama, Firmansyah Putra Pratama, Alfan Rizaldy Sari, Dewi Mutiara Marta, Bayu Sandi Armono, R. Haryo Dwito Politekn Elekt Negeri Surabaya Dept Informat & Comp Engn Surabaya Indonesia Univ Pembangunan Nasl Vet Jawa Timur Fac Comp Sci Data Sci Dept Surabaya Indonesia Inst Teknol Sepuluh Nopember Surabaya Fac Marine Technol Dept Ocean Engn Surabaya Indonesia

ISBN: (纸本)9798350392005;9798350391992

Breakwater construction in Indonesia still relies on divers to direct the placement of rock armour units, which is risky and time-constrained. This research aims to replace the diver's task with a deep learning-based vision system using YOLO-based deep learning models. The system utilizes image pre-processing technology by applying histogram equalization (HE) techniques to improve image quality before the detection process. This research evaluates the performance of the YOLO-based deep learning models in detecting armour units in real-time with a focus on various environmental conditions, which are clear and murky water. The analysis reveals clear water consistently supports higher average frame rates (FPS) compared to murky water, maintaining efficient frame processing across all models. In murky water, histogram equalization significantly enhances detection accuracy from 60% to 80% for YOLOv4-tiny and YOLOv7-tiny, demonstrating its effectiveness in challenging conditions. Notably, accuracy remains at 100% for all models in clear water, underscoring their robust performance under optimal visibility conditions.

关键词： breakwater armour units deep learning image pre-processing real-time detection

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real-time deep learning-based image processing for pose estimation and object localization in autonomous robot applications

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INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY 2023年第3-4期127卷 1905-1919页

作者： Upadhyay, Ritam Asi, Abhishek Nayak, Pravanjan Prasad, Nidhi Mishra, Debasish Pal, Surjya K. Birla Inst Technol Mesra Dept Elect & Commun Engn Mesra 835215 India Indian Inst Technol Kharagpur Ctr Excellence Adv Mfg Technol Kharagpur 721302 W Bengal India Birla Inst Technol Mesra Dept Mech Engn Mesra 835215 India Indian Inst Technol Kharagpur Adv Technol Dev Ctr Kharagpur 721302 W Bengal India Indian Inst Technol Kharagpur Dept Mech Engn Kharagpur 721302 W Bengal India

Artificial intelligence (AI) is shaping manufacturing to make it smarter, intelligent, and autonomous. Presently, flexible robots have been introduced that collaborate with humans on the shop floor to enhance productivity and efficiency. Object classification and pose estimation in an autonomous robotic system are crucial problems for proper grasping. Extensive research is being conducted to achieve low-cost, computationally efficient, and real-time assessments. However, most of the existing approaches are computationally expensive and constrained to previous knowledge of the 3D structure of an object. This article presents an AI-based solution, which generalizes cuboid- and cylindrical-shaped objects' grasping in real-time, irrespective of the dimensions. The AI algorithm has achieved an average precision of 89.44% and 82.43% for cuboid- and cylindrical-shaped objects. It is identified without the knowledge of the objects' 3D model. The pose is estimated in real-time, accurately. The integrated solution has been implemented in a robotic system fitted with two grippers, a conveyor system, and sensors. Results of several experiments have been reported in this article, which validates the solution. The proposed methodology has achieved 100% accuracy during our experiments to grasp objects on the conveyor belt.

关键词： Artificial intelligence real-time pose estimation real-time object detection Robotic applications real-time gripper selection

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Distributed deep learning and Energy-Efficient real-time image processing at the Edge for Fish Segmentation in Underwater Videos

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IEEE ACCESS 2022年 10卷 117796-117807页

作者： Jahanbakht, Mohammad Xiang, Wei Waltham, Nathan J. Azghadi, Mostafa Rahimi James Cook Univ Ctr Trop Water & Aquat Ecosyst Res TropWATER Townsville Qld 4811 Australia James Cook Univ Coll Sci & Engn Townsville Qld 4811 Australia La Trobe Univ Sch Comp Engn & Math Sci Melbourne Vic 3086 Australia

Using big marine data to train deep learning models is not efficient, or sometimes even possible, on local computers. In this paper, we show how distributed learning in the cloud can help more efficiently process big data and train more accurate deep learning models. In addition, marine big data is usually communicated over wired networks, which if possible to deploy in the first place, are costly to maintain. Therefore, wireless communications dominantly conducted by acoustic waves in underwater sensor networks, may be considered. However, wireless communication is not feasible for big marine data due to the narrow frequency bandwidth of acoustic waves and the ambient noise. To address this problem, we propose an optimized deep learning design for low-energy and real-time image processing at the underwater edge. This leads to trading the need to transmit the large image data, for transmitting only the low-volume results that can be sent over wireless sensor networks. To demonstrate the benefits of our approaches in a real-world application, we perform fish segmentation in underwater videos and draw comparisons against conventional techniques. We show that, when underwater captured images are processed at the collection edge, 4 times speedup can be achieved compared to using a landside server. Furthermore, we demonstrate that deploying a compressed DNN at the edge can save 60% of power compared to a full DNN model. These results promise improved applications of affordable deep learning in underwater exploration, monitoring, navigation, tracking, disaster prevention, and scientific data collection projects.

关键词： Big marine data deep learning edge computing fish image segmentation Internet of Underwater Things real-time video processing U-Net convolutional neural networks

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real time Quality Detection and Automatic Recognition of Concrete Structures: image processing Algorithm Based on deep learning 4

Real Time Quality Detection and Automatic Recognition of Con...

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4th International Conference on Machine learning and Big Data Analytics for IoT Security and Privacy, SPIoT 2023

作者： Liu, Bing College of Intelligent Manufacturing and Automobil Chongqing Vocational College of Transportation Chongqing402247 China

The safety and durability of concrete structure is an important issue in engineering quality management. In this paper, an image processing algorithm based on deep learning is proposed to realize real-time quality inspection and automatic defect identification of concrete structures. This algorithm uses the Convolutional Neural Network (CNN) to automatically extract the features of concrete surface quality images, and then identify the existence of defects, thus improving the detection efficiency and accuracy. In this paper, for this method, data samples with different specific structures are collected and manually labeled to the data set;then, a multi-layer CNN model with convolution layer, pool layer and full connection layer is designed to train the model, and then image enhancement technology is used to reduce information noise, and data enhancement technology is used to improve the problem-solving ability of the model. In addition, the strategy of Dropout is used to close some nodes to reduce parameters and prevent over-fitting, and the learning rate is adjusted to optimize the classification effect. In addition, this study constructs an all-weather real-time detection framework, including data acquisition, preprocessing, feature extraction, classification and identification and decision-making alarm system, to ensure the rapid positioning of the detection system. To sum up, the results of this study show that the deep learning image processing algorithm has good contrast performance in the field of real-time quality inspection of concrete structures. CNN model has better performance than GAN (Generative Adversarial Network) and LSTM (Long Short-Term Memory) models in detection time, defect identification resolution and detection accuracy. The maximum detection time is 366ms and the shortest is 213 ms. The successful development of this algorithm provides a new method for automatic detection of concrete structure quality, which has important application value i

关键词： Decision making

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real-time image processing and deep learning 2024

Real-Time Image Processing and Deep Learning 2024

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real-time image processing and deep learning 2024

ISBN: (纸本)9781510673861

The proceedings contain 20 papers. The topics discussed include: edge deployed satellite image classification with TinEViT a X-Cube-AI compatible efficient vision transformer;a design space exploration framework for deployment of resource-constrained deep neural networks;IoT-enabled unmanned traffic management system with dynamic vision-based drone detection for sense and avoid coordination;exploring action recognition in endoscopy video datasets;integrating image-based LLMs on edge-devices for underwater robotics;age-based clustering of seagrass blades using AI models;layered convolutional neural networks for multi-class image classification;eyeball tracking in closed eyes from shadows;CAEN: efficient adversarial robustness with categorized ensemble of networks;improving real-time security screening;and coupling deep and handcrafted features to assess smile genuineness.

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Illuminating Solar Potential: A real-time deep learning, IoT, and image processing Approach to Ground-Based Cloud Movement Forecasting for Enhanced Solar Energy Management 1

Illuminating Solar Potential: A Real-Time Deep Learning, IoT...

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1st International Conference for Women in Computing, InCoWoCo 2024

作者： Perera, P.K.M. Karunarathna, K.B.S. Hewawalpita, S. Dassanayake, S.M. University of Moratuwa Faculty of Business Department of Decision Science Sri Lanka

ISBN: (纸本)9798331518943

Cloud movement impacts the performance of photovoltaic (PV) power plants by causing sudden fluctuations in output power, leading to voltage instability in connected electricity networks. This paper introduces a novel and cost-effective deep learning solution for forecasting shortterm cloud movement to regulate PV power output. The methodology involves capturing raw image data through a PyCamera connected to a Raspberry Pi, followed by rigorous pre-processing and cloud detection techniques. Using colourbased segmentation, morphological operations, and contour analysis, the developed pipeline accurately identifies cloud regions in images and predicts their movement for the subsequent time interval. The proposed algorithm is designed to be efficient, lightweight, and suitable for deployment on low-cost processors. This study successfully implements and optimizes a cloud detection system for resource-constrained environments using the Raspberry Pi 4 Model B. The method significantly improves inference speed and model efficiency while addressing the high latency constraints associated with embedded devices with execution periods measured in seconds. The model performance metrics validate the approach, with an accuracy of 89.84%, indicating the practical potential of the model for real-time applications, offering a promising solution towards the integration of effective cloud forecasting into cost-sensitive energy management systems. © 2024 IEEE.

关键词： image segmentation

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real time image processing and Operation Management of Multicultural Communication Platforms Based on deep learning

Real Time Image Processing and Operation Management of Multi...

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2023 International Conference on Mechatronics and Intelligent Control, ICMIC 2023

作者： Zhang, Chi Guo, Jiaqi Huanghe S&T University Jiyuan454650 China International College Zhengzhou University Zhengzhou450001 China

ISBN: (纸本)9781510668393

The promotion and utilization of modern technology has made intercultural exchanges more frequent and deep, andsecure image encryption and processing are hot issues for multicultural IOT communication platforms in the context ofglobalization for smart cities. In this paper, the re-al-time image encryption test and resource scheduling optimizationanalysis of multicultural communication platform are carried out based on convolutional neural network. First, weencrypt the image based on the layered image encryption technology of the chaotic system and the Feistel network. Secondly, through different strength encryption algorithms and different convolutional network structures, the encryptedimages are classified and analyzed, and the encryption effect of the images is tested by the classification accuracy. Inaddition, we construct a task-resource scheduling model and design scheduling optimization schemes for the model fromthe aspects of data flow and control flow, so as to realize the mapping of network model to the IOT cloud plat-formandthe reasonable scheduling of task resources on the software and hardware side. © 2023 SPIE.

关键词： deep learning

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real time Quality Detection and Automatic Recognition of Concrete Structures: image processing Algorithm Based on deep learning

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Procedia Computer Science 2024年 243卷 793-800页

作者： Bing Liu College of Intelligent Manufacturing and Automobil Chongqing Vocational College of Transportation Chongqing 402247 China

The safety and durability of concrete structure is an important issue in engineering quality management. In this paper, an image processing algorithm based on deep learning is proposed to realize real-time quality inspection and automatic defect identification of concrete structures. This algorithm uses the Convolutional Neural Network (CNN) to automatically extract the features of concrete surface quality images, and then identify the existence of defects, thus improving the detection efficiency and accuracy. In this paper, for this method, data samples with different specific structures are collected and manually labeled to the data set; then, a multi-layer CNN model with convolution layer, pool layer and full connection layer is designed to train the model, and then image enhancement technology is used to reduce information noise, and data enhancement technology is used to improve the problem-solving ability of the model. In addition, the strategy of Dropout is used to close some nodes to reduce parameters and prevent over-fitting, and the learning rate is adjusted to optimize the classification effect. In addition, this study constructs an all-weather real-time detection framework, including data acquisition, preprocessing, feature extraction, classification and identification and decision-making alarm system, to ensure the rapid positioning of the detection system. To sum up, the results of this study show that the deep learning image processing algorithm has good contrast performance in the field of real-time quality inspection of concrete structures. CNN model has better performance than GAN (Generative Adversarial Network) and LSTM (Long Short-Term Memory) models in detection time, defect identification resolution and detection accuracy. The maximum detection time is 366ms and the shortest is 213 ms. The successful development of this algorithm provides a new method for automatic detection of concrete structure quality, which has important application value

关键词： Concrete Structure real-time Quality Inspection Automatic Recognition deep learning image processing Algorithms

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