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Enhancing Autonomous Vehicle Navigation with Real-Time Object Detection Using Convolutional Neural Networks

Enhancing Autonomous Vehicle Navigation with Real-Time Objec...

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international conference on Contemporary Computing and Informatics (IC3I)

作者： Arnav Kotiyal Zaid Alsalami Nandini Shirish Boob Arti Badhoutiya T Mounika Muthuswamy Jayanthi Prateek Chaturvedi Department of Computer Science & Engineering Graphic Era Deemed to be University Dehradun Department of computers Techniques engineering College of technical engineering The Islamic university Najaf Iraq Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai Tamil Nadu Department of Electrical Engineering GLA University Mathura Department of Computer Science and Engineering MLR Institute of Technology Hyderabad Telangana Department of Electronics and Communication Engineering New Horizon College of Engineering Bangalore Amity School of Engineering & Technology Amity University Greater Noida Uttar Pradesh India

ISBN: (数字)9798350350067

ISBN: (纸本)9798350350074

Autonomous vehicles’ (AVs) capacity to identify and categorise things in real-time is crucial to their development for the sake of efficient and safe navigation. Because of its exceptional capability to learn spatial feature hierarchies automatically, Convolutional Neural Networks (CNNs) have become an indispensable tool for object identification in intricate visual recognition problems. this research offers a thorough analysis of convolutional neural networks (CNNs) used for autonomous vehicle object identification in real-time, with an emphasis on improving detection accuracy and speed. We compare the processing speed, detection accuracy, and computational efficiency of several CNN designs, such as YOLO (You Only Look Once), SSD (Single Shot Multibox Detector), and Faster R-CNN. In addition, to address the demanding realtime needs of autonomous driving systems, we suggest CNN design improvements that use hardware acceleration methods like GPU and TPU optimisations. the experimental findings show that our suggested method significantly reduces detection latency while maintaining accuracy, which makes it suitable for use in real-world AV settings. Autonomous vehicle technology advances thanks to this study’s results, which aid in building scalable and reliable object identification algorithms.

关键词： YOLO Accuracy Navigation Graphics processing units Real-time systems Convolutional neural networks Object recognition Autonomous vehicles Optimization Hardware acceleration

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Classification and Prediction of Lung Cancer with Histopathological Images Using VGG-19 Architecture 1

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5th IFIP TC 12 international conference on Computational Intelligence in Data science, ICCIDS 2022

作者： Saranya, N. Kanthimathi, N. Boomika, S. Bavatharani, S. Karthick raja, R. ECE Bannari Amman Institute of Technology Sathyamangalam India

ISBN: (数字)9783031163647

ISBN: (纸本)9783031163630

In recent times, for the diagnosis of several diseases, many computer-Aided Diagnosis (CAD) systems have been designed. Among many life-threatening diseases, lung cancer is one of the leading causes of cancer-related deaths in humans worldwide. It is a malignant lung tumor distinguished by the uncontrolled growth of cells in the tissues of the lungs. Diagnosis of cancer is a challenging task due to the structure of cancer cells. Predicting lung cancer at its initial stage plays a vital role in the diagnosis process and also increases the survival rate of patients. People with lung cancer have an average survival rate ranging from 14 to 49% if they are diagnosed in time. the current study focuses on lung cancer classification and prediction based on Histopathological images by using effective deep learning techniques to attain better accuracy. For the classification of lung cancer as Benign, Adenocarcinoma, or Squamous Cell Carcinoma, some pre trained deep neural networks such as VGG-19 were used. A database of 15000 histopathological images was used in which 5000 benign tissue images and 10000 malignant lung cancer-related images to train and test the classifier. the experimental results show that the VGG-19 architecture can achieve an accuracy of 95%. © 2022, IFIP international Federation for Information Processing.

关键词： Diseases

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Live Social Spacing Tracker Based on Domain Detection 1

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5th IFIP TC 12 international conference on Computational Intelligence in Data science, ICCIDS 2022

作者： Agarwal, Divyank Mehta, Monark Paramanandham, Nirmala Vellore Institute of Technology Chennai India

ISBN: (数字)9783031163647

ISBN: (纸本)9783031163630

Corona virus Disease-2019 (COVID-19) is caused by infection with the severe acute respiratory syndrome (SARS) coronavirus. In this COVID 19 pandemic, the virus spread bringing the whole world to downfall. this disease can spread through the slightest touch, breathing the same air or using same basic things like clothes, hairs, combs, etc. these viruses can live for hours even without a host body. To prevent the spread, the world was put on lockdown, and people were constrained to their homes, but human life cannot go on without interaction. We need a better way of preventing the spread of this type of disease. Hence a detector is proposed for measuring the social distance between the people. this social distancing detector can track the people who are not following social distancing norms, then that person can be tracked down or the person is marked in red and triggers the warning. In this paper we will be using computer Neural Network (CNN) to process our images and videos because CNN is a type of artificial neural network that is used in recognition of large pixel videos or images. the proposed technique can be the perfect way to help the person while people carry out their daily tasks. From the evaluation parameters it is proven that the proposed technique yields better results when compared to the state- of- the art techniques. © 2022, IFIP international Federation for Information Processing.

关键词： Neural networks

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Exploring the Incremental Improvements of YOLOv7 over YOLOv5 for Character Recognition 14th

Exploring the Incremental Improvements of YOLOv7 over YOL...

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12th international Advanced Computing conference, IACC 2022

作者： Boby, Alden Brown, Dane Connan, James Marias, Marc Department of Computer Science Rhodes University Grahamstown South Africa

ISBN: (纸本)9783031356438

Technological advances are being applied to aspects of life to improve quality of living and efficiency. this speaks specifically to automation, especially in the industry. the growing number of vehicles on the road has presented a need to monitor more vehicles than ever to enforce traffic rules. One way to identify a vehicle is through its licence plate, which contains a unique string of characters that make it identifiable within an external database. Detecting characters on a licence plate using an object detector has only recently been explored. this paper uses the latest versions of the YOLO object detector to perform character recognition on licence plate images. this paper expands upon existing object detection-based character recognition by investigating how improvements in the framework translate to licence plate character recognition accuracy compared to character recognition based on older architectures. Results from this paper indicate that the newer YOLO models have increased performance over older YOLO-based character recognition models such as CRNET. © 2023, Springer Nature Switzerland AG.

关键词： Object detection

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Optical Analysis of the UPQC using PI Controller in Power flow System 7

Optical Analysis of the UPQC using PI Controller in Power fl...

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7th international conference on Advanced Computing and Communication Systems, ICACCS 2021

作者： Karthik, Mamidala Vijay Chakravarthi, M.Kalyan Yapanto, Lis M Selvapandian, D. Harish, R. Subramani, Karthick Noida International University Department of Electrical Engineering Uttar Pradesh Greater Noida India VIT-AP University School of Electronics Engineering Amaravathi India Universitas Negeri Gorontalo Faculty of Fishery and Marine Science Indonesia Karpagam Academy of Higher Education Department of Computer Science and Engineering Tamil Nadu India Sns College of Engineering Department of Electrical and Electronics and Engineering Tamil Nadu India Srm Institute of Science and Technology Department of Computer Science and Engineering Uttar Pradesh 201204 India

ISBN: (纸本)9781665405201

this examination depends on a three-phase unified control quality conditioner (UPQC) to extend control quality in a three-phase distribution framework. It is nice for day-to-day trade and domestic sectors. Researchers have endeavored and executed different advantageous advances to induce freed of both voltage and current consonant issues, making strides the quality of control given by the control framework. UPQC was made up of a arrangement and a shunt converter that were associated back-to-back employing a common dc-link capacitor. this paper works by actualizing UPQC control quality to compensate for voltage and current sounds issues. UPQC uses the PI controller, which calculates error value differently between the measured and desired set points. the conventional UPQC tool is limited to the PI controller's DC link voltage law. Also, it presents a three-phase transformation using a special multi-winding transformer connection scheme. A MATLAB simulation is utilized in this paper to demonstrate how the UPQC demonstrate can decrease the percent thD in source voltage, source current, and stack voltage waveforms actuated by nonlinear loads. the execution of UPQC is analyzed with a nonlinear load, and simulation studies utilizing MATLAB/Simulink prove the suitable execution of UPQC. © 2021 IEEE.

关键词： Power quality

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A two-stage network based on edge information for visual anomaly detection 23

A two-stage network based on edge information for visual ano...

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Proceedings of the 2023 7th international conference on advances in Image Processing

作者： Di Chen Juan Xu Nanjing University of Aeronautics and Astronautics College of Computer Science and Technology ChinaJiangsuNanjingNanjing University of Aeronautics and AstronauticsCollege of Computer Science and Technology China

ISBN: (纸本)9798400708275

Reconstruction-based approaches attract increasing attention in unsupervised visual anomaly detection. Such methods rely on the assumption that reconstruction models trained using only normal images are not well reconstructed for anomalous data, so that the anomalous regions can be identified by computing the differences in the reconstructions. However, it's usually challenging to manage the model's generalization boundaries in practice. the model with a poor generalization capability will produce additional image differences in the normal regions, while the model with an overly strong generalization capability can even well reconstruct the abnormal regions, making the abnormal regions less distinguishable from the normal regions. In this paper, we propose a two-stage reconstruction network to solve the above problems, which can generate high-accuracy and anomaly-free reconstruction. Specifically, our two-stage anomaly detection network consists of the edge extraction sub-network and the image restoration sub-network. the first sub-network generates anomaly-free grayscale edges of the image as intermediate results, and then the second one recovers normal color and low-frequency information for the intermediate results. the proposed network can generate high-accuracy and anomaly-free reconstructions to maximize the difference between the reconstruction errors of normal and abnormal samples, which can ultimately improve the accuracy of anomaly detection. Experiments on the dataset MVTec AD show that our proposed method achieves competitive results in anomaly detection and anomaly localization (97.8% for detection and 97.7% for localization, AUROC).

关键词： Anomaly Detection Anomaly Localization Edge Information Two-stage network

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An Improved Ensemble Extreme Learning Machine Classifier for Detecting Diabetic Retinopathy in Fundus Images 5th

An Improved Ensemble Extreme Learning Machine Classifier for...

引用

5th IFIP TC 12 international conference on Computational Intelligence in Data science, ICCIDS 2022

作者： Vinayaki, V. Desika Kalaiselvi, R. Noorul Islam Centre For Higher Education Tamil Nadu Kumaracoil India Department of Computer Science and Engineering RMK College of Engineering and Technology Puduvoyal Gummidipoondi India

ISBN: (纸本)9783031163630

this paper presents an automatic diabetes Retinopathy (DR) detection system using fundus images. the proposed automatic DR screening model saves the time of the ophthalmologist in disease diagnosis. In this approach, the segmentation is conducted using an improved watershed algorithm and Gray Level Co-occurrence Matrix (GLCM) is used for feature extraction. An improved Ensemble Extreme Learning Machine (EELM) is used for classification and its weights are tuned using the Crystal Structure Algorithm (CRYSTAL) algorithm which also optimizes the loss function of the EELM classifier. the experiments are conducted using two datasets namely DRIVE and MESSIDOR by comparing the proposed approach against different state-of-art techniques such as Support Vector Machine, VGG19, Ensemble classifier, and Synergic Deep Learning model. When compared to existing methodologies, the proposed approach has sensitivity, specificity, and accuracy scores of 97%, 97.3%, and 98%, respectively. © 2022, IFIP international Federation for Information Processing.

关键词： Support vector machines

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Assessing Layer Normalization with BraTS MRI Data in a Convolution Neural Net 1

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5th IFIP TC 12 international conference on Computational Intelligence in Data science, ICCIDS 2022

作者： Rawat, Akhilesh Kumar, Rajeev Lab School of Computer and Systems Sciences Jawaharlal Nehru University New Delhi110 067 India

ISBN: (数字)9783031163647

ISBN: (纸本)9783031163630

Deep learning-based Convolutional Neural Network (CNN) architectures are commonly used in medical imaging. Medical imaging data is highly imbalanced. A deep learning architecture on its own is prone to overfit. As a result, we need a generalized model to mitigate total risk. this paper assesses a layer normalization (LN) technique in a CNN-based 3D U-Net for faster training and better generalization in medical imaging. Layer Normalization (LN) is mostly used in Natural Language Processing (NLP) tasks such as question-answering, handwriting sequence generation, etc. along with Recurrent Neural Network (RNN). the usage of LN is yet to be studied in case of medical imaging. In this context, we use brain MRI segmentation and train our model with LN and without normalization. We compare both models and our LN-based model gives 32 % less validation loss over without normalization-based model. We achieve validation dice scores of unseen input data passes to LN based model of 0.90 (7.5 % higher than without normalization) for edema, 0.74 (12.5 % higher than without normalization) for non-enhancing tumor and 0.95 (1.5 % higher than without normalization) for enhancing tumor. © 2022, IFIP international Federation for Information Processing.

关键词： Convolution

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Enhanced Residual Connections Method for Low Resolution Images in Rice Plant Disease Classification 5th

Enhanced Residual Connections Method for Low Resolution Imag...

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5th IFIP TC 12 international conference on Computational Intelligence in Data science, ICCIDS 2022

作者： Sathya, K. Rajalakshmi, M. Coimbatore Institute of Technology Coimbatore641014 India

ISBN: (纸本)9783031163630

Recent advancements in both raw computing powers as well as the capabilities of cameras in recent times, it has now become possible to capture images of very high quality. this improvement however does come at the cost of the overall space required to store such high-quality images. One possible solution to this problem would be the storage of the images in low resolution and then upsampling the images to obtain the original resolutions. Despite advances in computer vision, deep learning models for super resolution have been introduced to address the challenges and thus provide promising improved performance results. this paper will explore a novel self-supervised deep learning architecture entitled Enhanced Residual Connections for Image super resolution (ERCSR) that is capable of upsampling extremely low-quality images to their higher quality. Experiments on the different data sets such as DIV2k and rice plant images are made to evaluate this model and experimental results shows that our method outperforms image enhancement. Furthermore, rice plant images are subsequently passed through disease classification layers and achieves desired accuracy for super resolution images. © 2022, IFIP international Federation for Information Processing.

关键词： Image enhancement

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Identification of the Driver Demand Torque Map in Engine ECU Based on Unscented Kalman Filter 7th

Identification of the Driver Demand Torque Map in Engine ECU...

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7th international conference on advances in Control and Optimization of Dynamical Systems, ACODS 2022

作者： Ruan, Diwang Baldyga, Julia Electronic Measurement and Diagnostic Technology TU Berlin 10587 Germany School of Electronic Engineering and Computer Science TU Berlin 10587 Germany

In the electronic control unit (ECU) of a gasoline engine, there are more than a thousand of maps to define the nonlinear and difficult physically-modeled relation between the control input, output, and condition parameters. these maps are normally stored as 1D or 2D lookup tables in ECU after calibration and become part of the controlled object. To implement a high-quality up-layer controller, identification for the actual controlled plant is very necessary, including the inherently embedded control maps. In this paper, the driver demand torque map (DDT map) was taken as an example. At first, the DDT map was inverted and polynomial model was adopted to characterize the relation between torque demand, engine speed and pedal position, with minimum measurement data to initialize the polynomial model coefficients by regression fitting. After that, the fitted DDT map model was taken as a nonlinear system, the polynomial coefficients were taken as states and further recursively updated with unscented Kalman filter (UKF) once new measurements were obtained. Finally, the whole map was identified and compared with the original one. Two different kinds of DDT maps (economic-mode, sport-mode) were studied, and different orders of polynomial models and parameters fine-tuning were also addressed. Results confirm the feasibility and superiority of the UKF-based map identification. © 2022 Elsevier B.V.. All rights reserved.

关键词： Driver demand torque map Gasoline engine Map identification Unscented kalman filter(UKF)

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