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Deep Learning Models for Moving Vehicle Detection in Traffic Surveillance Video for Smart City applications 1

2nd International Conference on Electronic Governance with Emerging Technologies, EGETC 2023

作者： Risha, K. Jude Hemanth, D. Department of ECE Karunya Institute of Technology and Sciences Coimbatore India

ISBN: (数字)9783031439407

ISBN: (纸本)9783031439391

In the last few decades, CCTV surveillance has become a piece of unavoidable equipment in public places. The most important application is for traffic monitoring. Recently, accident cases have been rapidly increasing day by day. So, continuous analysis of vehicles is a challenging task. Developing an algorithm to find a specific vehicle or pedestrian is highly beneficial in such cases. Such a procedure can be done by video processing. Video processing and its applications have improved over the last few years. The advancements in this area are helpful in numerous fields. At the same time, the intervention of artificial intelligence also has a relevant role in this area. Machine learning has also become essential and valuable in various complex cases. image classification using artificial intelligence provides a highly accurate result rather than any other classification method. This paper mainly compares different convolutional neural networks on moving objects detected by background subtraction and GMM with the morphological operation. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Convolutional neural networks

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Profiling Language Sign Using Deep Learning 10

Profiling Language Sign Using Deep Learning

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10th International Conference on Wireless networks and Mobile Communications, WINCOM 2023

作者： Elbaghazaoui, Bahaa Eddine Amnai, Mohamed Fakhri, Youssef Choukri, Ali Gherabi, Noreddine IbnTofail University Laboratory of Computer Sciences Faculty of Sciences Kenitra Kenitra Morocco Sultan Moulay Slimane University National School of Applied Sciences Khouribga Morocco

ISBN: (纸本)9798350329674

Large segments of society place a high value on sign language as a distinct communication language. In computer vision, gesture detection is still a developing phenomenon and a hot topic. Aside from its many applications in immersive game technology and human-computer interaction, gesture recognition has many applications in deaf-mute sign language interpretation. In order to profile language signs, we created a model of image processing that recognizes Arabic hand sign language based on artificial neural networks, starting with the data collection process and continuing with data profiling, then using the deep learning model to train and identify gestures, all the way to the presentation of the results. We offer a method to help with the conversion of sign language into voice and text-based formats. The outcomes we have achieved are crucial, important and significant for our next achievements. © 2023 IEEE.

关键词： neural networks

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Convolutional neural networks for Engineering Design Validation

Convolutional Neural Networks for Engineering Design Validat...

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applications of Machine Learning 2024

作者： Lowandy, K. Kelliher, S. Le, D. Molinari, C. Harris, I. Unger, L. Fink, R. Shemelya, C. Robinette, P. University of Massachusetts Lowell Raytheon UMass Lowell Research Institute LowellMA United States Applied NanoTech Inc United States

ISBN: (纸本)9781510679368

The growth of artificial intelligence has led to the widespread use of convolutional neural networks (CNNs) for computer vision applications, traditionally for binary and categorical classification tasks. However, there remains untapped potential for advancing computer vision through deep learning in regression tasks. Design engineers across many disciplines use computer-aided design software to model their designs. These computer-integrated designs often require machinery for construction or fabrication. For many engineering designs, precision and tolerancing is essential for the proper function and performance of the design. The engineering process typically involves manual testing and parameter measurements to ensure the proper function of the design before it is marketed. However, training a neural network to automate these tests and provide accurate numeric estimates of system parameters without manual intervention can significantly increase efficiency and decrease the time to market for many products. This shift from manual to automated testing allows for a heightened focus on innovation and project development while minimizing the time and resource dedication for validation. This article outlines the implementation of CNN models designed to enhance the efficiency of manually validating engineered projects. Our approach involves utilizing computer-aided design simulation image captures as training data for our pipeline. We integrate a real-time color-filtering and fiducial rotation scaling normalization process on any fabricated design image. Through these pre-processing methods, our algorithm can perceive these images in a consistent manner with simulation images from the model training. Our current model is trained with only 1020 simulation images and achieves a 1.99% average training prediction error on this dataset after training. Before, our errors were a 10.51% average error in our initial model implementation and 3.63% in our second implementation. On o

关键词： Precision engineering

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Bio-Inspired Electronic Eyes and Synaptic Photodetectors for Mobile artificial Vision

IEEE Journal on Flexible Electronics

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IEEE Journal on Flexible Electronics 2022年第2期1卷 76-87页

作者： Choi, Changsoon Seung, Hyojin Kim, Dae-Hyeong Seoul02792 Korea Republic of Seoul08826 Korea Republic of School of Chemical and Biological Engineering Institute of Chemical Processes Seoul National University Seoul08826 Korea Republic of

Conventional imaging and data processing devices are not ideal for mobile artificial vision applications, such as vision systems for drones and robots, because of the heavy and bulky multilens optics in the camera modules. Furthermore, the physically isolated image data processing units of conventional systems induce large power consumption and data latency. For mobile artificial vision applications, electronic eyes, including neuromorphic ones, have been developed inspired by biological eyes and neural networks. Here, we summarize the development of such bio-inspired electronic eyes and synaptic photodetectors (PDs). Bio-inspired electronic eyes, typically consisting of curved image sensor arrays, enable aberration-free imaging and module size miniaturization in addition to other advantageous optical features, such as wide field-of-view and deep depth-of-field. Furthermore, photodetecting devices with synaptic properties can efficiently enhance image contrast because of photon-triggered synaptic plasticity. Therefore, the signal-to-noise ratio of the acquired image can be enhanced, which facilitates efficient image recognition for machine vision. A brief summary of the remaining challenges and prospects concludes this review. © 2022 Institute of Electrical and Electronics Engineers. All rights reserved.

关键词： Cameras

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Medical Transformers for Boosting Automatic Grading of Colon Carcinoma in Histological images 1

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22nd International Conference on image Analysis and processing (ICIAP)

作者： Carcagni, Pierluigi Leo, Marco Signore, Luca Distante, Cosimo CNR ISASI Ecotekne Campus via Monteroni I-73100 Lecce Italy Univ Salento Via Monteroni I-73100 Lecce Italy

ISBN: (数字)9783031431487

ISBN: (纸本)9783031431470;9783031431487

Developing computer-aided approaches for cancer diagnosis and grading is receiving an uprising demand since this could take over intra- and inter-observer inconsistency, speed up the screening process, allow early diagnosis, and improve the accuracy and consistency of the treatment planning processes. The third most common cancer worldwide and the second most common in women is ColoRectal Cancer (CRC). Grading CRC is a key task in planning appropriate treatments and estimating the response to them. Automatic systems have the potential to speed up and make it more robust but, unfortunately, the most recent and promising machine learning techniques have not been applied for automatic CRC grading so far. For example, there is no work exploiting transformer networks, which outperform convolutional neural networks (CNN) and are replacing them in many applications, for CRC detection and grading at a large scale. To fill this gap, in this work, a transformer-based network endowed with an additional control mechanism in the self-attention module is exploited to understand discriminative regions in large histological images. These relevant regions have been used to train the most suited Convolutional neural Network (as emerged from recent research findings) for the automatic grading of CRC. The experimental proofs on the largest publicly available CRC dataset demonstrated marked improvement with respect to the leading state-of-the-art approaches relying on CNN.

关键词： Colon carcinoma artificial intelligence Deep learning Transformer networks Histological diagnosis

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Comparison of image processing techniques for defect detection 1

Comparison of image processing techniques for defect detecti...

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1st International Workshop of Young Scientists on artificial Intelligence for Sustainable Development, AISD 2024

作者： Kovalskyi, Semen Koval, Vasyl West Ukrainian National University 11 Lvivska Str. Ternopil46009 Ukraine

Defect detection is a crucial quality control process in the manufacturing industry, aimed at identifying and classifying imperfections or anomalies in products before they reach customers. Traditional manual inspection methods are time-consuming, labor-intensive, and prone to human error. This paper provides a comprehensive overview of image-based defect detection algorithms, including traditional image processing techniques, machine learning algorithms, and deep learning models. The study analyzes the strengths, limitations, and performance of each approach across various applications and datasets. The results demonstrate that while traditional methods and machine learning algorithms offer reliable defect detection, deep learning models, particularly convolutional neural networks (CNNs), achieve exceptional accuracy and robustness. However, deep learning models require significant computational resources and large amounts of labeled data for training. The paper highlights the importance of selecting the most appropriate approach based on specific application requirements, data characteristics, and computational constraints. Furthermore, it discusses future research opportunities, such as developing more robust and generalized algorithms, leveraging multi-modal data, improving model interpretability, and enabling real-time and edge computing solutions. © 2024 Copyright for this paper by its authors.

关键词： Computer vision

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Metasurface-enabled on-chip multiplexed diffractive neural networks in the visible

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Light(Science & applications) 2022年第7期11卷 1443-1453页

作者： Xuhao Luo Yueqiang Hu Xiangnian Ou Xin Li Jiajie Lai Na Liu Xinbin Cheng Anlian Pan Huigao Duan National Research Center for High-Efficiency Grinding College of Mechanical and Vehicle EngineeringHunan UniversityChangsha 410082China Institute of Precision Optical Engineering School of Physics Science and EngineeringTongji UniversityShanghai 200092China Advanced Manufacturing Laboratory of Micro-Nano Optical Devices Shenzhen Research InstituteHunan UniversityShenzhen 518000China 2nd Physics Institute University of StuttgartPfaffenwaldring 5770569 StuttgartGermany Max Planck Institute for Solid State Research Heisenbergstrasse 170569 StuttgartGermany Greater Bay Area Institute for Innovation Hunan UniversityGuangzhou 511300China

Replacing electrons with photons is a compelling route toward high-speed,massively parallel,and low-power artificial intelligence ***,diffractive networks composed of phase surfaces were trained to perform machine learning tasks through linear optical ***,the existing architectures often comprise bulky components and,most critically,they cannot mimic the human brain for ***,we demonstrate a multi-skilled diffractive neural network based on a metasurface device,which can perform on-chip multi-channel sensing and multitasking in the *** polarization multiplexing scheme of the subwavelength nanostructures is applied to construct a multi-channel classifier framework for simultaneous recognition of digital and fashionable *** areal density of the artificial neurons can reach up to 6.25×10^(6)mm^(-2) multiplied by the number of *** metasurface is integrated with the mature complementary metal-oxide semiconductor imaging sensor,providing a chip-scale architecture to process information directly at physical layers for energy-efficient and ultra-fast image processing in machine vision,autonomous driving,and precision medicine.

关键词： visible autonomous complementary

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image Test Libraries for the in-field test of ultra-low-power devices 26

Image Test Libraries for the in-field test of ultra-low-powe...

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26th IEEE Latin American Test Symposium, LATS 2025

作者： Porsia, Antonio Perlo, Giacomo Ruospo, Annachiara Sanchez, Ernesto Politecnico di Torino DAUIN Turin Italy

ISBN: (纸本)9781665477635

In recent years, research and technology advancements have driven exponential growth in the adoption of artificial Intelligence (AI)-based systems, even in safety-critical contexts such as autonomous driving and healthcare applications. The joint effort of academia and industry has yielded techniques and standards with the objective of ensuring the safe operation of AI-based technology. In the specific context of Convolutional neural networks (CNNs) running on GPUs, image Test Libraries (ITLs) have been proposed as an effective method for performing on-line functional testing of GPU multipliers. This is achieved by launching the inference of a set of test images containing a set of ATPG-generated functional test patterns. However, while the demand for computational power for DNN models is constantly increasing, another branch of Machine Learning (ML) research, namely TinyML, focuses on minimizing the computational requirements of DNN models in order to bring AI capabilities to edge devices, whose constraints on power usage, memory space and processing power do not allow for the deployment of conventional DNN models. This research work aims to adapt the ITL technique to CNNs running on ultra-low-power edge hardware, while also overcoming some limitations of GPU ITLs. Experimental results demonstrate that a single test image generated using the proposed method is capable of detecting 96.01% of stuck-at faults occurring in the 32-bit integer multiplier of a RISC-V-based ultra-low-power System-on-Chip executing a quantized CNN. © 2025 IEEE.

关键词： Convolutional neural networks

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Workpiece Pose Classification Framework for Flexible Part Feeding Systems Using Machine Learning and Synthetic Datasets 10

Workpiece Pose Classification Framework for Flexible Part Fe...

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10th CIRP Conference on Assembly Technology and Systems, CATS 2024

作者： Kolditz, Torge Müller, Pitt Raatz, Annika Institute of Assembly Technology Leibniz University Hannover An der Universität 2 Garbsen30823 Germany

In previous work, a concept for a novel flexible part feeding system based on aerodynamic feeding was presented. In contrast to conventional part feeding systems, such as vibratory bowl feeders, aerodynamic part feeding systems use controlled pressurized air jets to reorient the workpieces. The proposed concept aims to increase flexibility and eliminate retooling times to meet the challenges of modern automated production with regard to increasing uncertainties, shorter product life cycles and higher cost pressure. Regarding these objectives, the determination of the workpiece poses in the feeding system requires flexible image processing, which will be enabled using machine learning and synthetic datasets. This work presents a framework which enables the classification of workpiece poses using a standard industrial camera and convolutional neural networks (CNN). The training of CNNs usually requires large datasets. However, in order to eliminate the need to create and label datasets of real images, which would cause machine downtimes and increase the retooling effort, the CNNs will be trained with synthetic datasets. Based on the CAD-data of the workpieces, artificial images of each pose are rendered using the open source engine Blender. Several CNN architectures are selected, trained with the artificial datasets and evaluated using real images of the workpieces. The results show that high classification accuracies of over 99 % can be achieved. It is concluded that the presented approach enables the classification of workpiece poses with high accuracy, while eliminating the need for the cumbersome creation of real image datasets. The approach is not limited to the use in the aerodynamic part feeding system but can be transferred to other applications in which the orientation of a workpiece has to be classified. © 2024 Elsevier B.V.. All rights reserved.

关键词： Convolutional neural networks

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Pest Detection and Identification Guided by Feature Maps 12

Pest Detection and Identification Guided by Feature Maps

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12th International Conference on image processing Theory, Tools and applications, IPTA 2023

作者： Chen, Miao Chen, Yanan Guo, Minghui Wang, Jianji Xi'an Jiaotong University National Key Laboratory of Human-Machine Hybrid Augmented Intelligence National Engineering Research Center for Visual Information and Applications Institute of Artificial Intelligence and Robotics China Xi'an Jiaotong University School of Software Engineering Xi'an China

ISBN: (纸本)9798350325416

Effective pest detection and identification are of great significance for agricultural activities, and morden machine learning methods, especially the deep neural network, undoubtedly provide convenient and effective ways for this issue. Most of the mainstream methods for pest detection require annotation of manual bounding box. However, the public pest datasets usually lack the labeling information. In this work, we propose a method for detecting pests with weak supervision. We obtain the key regions of the pest targets by deeply mining the information in the feature maps of the convolutional neural networks (CNNs). First, using the class activation mapping technique to generate activation maps of the target class during forward propagation of the model, and then obtaining the key regions of the target based on the hotspots extracted from the activation maps. In addition, our proposed method can be deployed as a general strategy to other CNNs, and experimental results demonstrate that our method can steadily improve the classification accuracy of the model. By applying our method to the single Resnet50, we can achieve 74.27% classification accuracy on the publicly accessible IP102 dataset. Another attribution of this paper is that we propose a lightweight approach to find solutions for pest detection in agricultural automation applications. © 2023 IEEE.

关键词： Deep neural networks

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