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Face detection and Classification using Convolutional Neural Network with Channel Attention Module

Face Detection and Classification using Convolutional Neural...

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Mobile Networks and Wireless Communications (ICMNWC), IEEE international conference on

作者： Zainab abed Almoussawi K Sudheer Kumar T. Veena Myasar Mundher Adnan Muhammed Basim Jabar College of Islamic Science Ahl Al Bayt University karbala Iraq Zainb School of Computer Science & Artificial Intelligence SR University Warangal Telangana India Department of Information Technology S.A. Engineering College Chennai India The Islamic University Najaf Iraq National University of Science and Technology Dhi Qar Iraq

Automatic face detection is one of the most challenging in computer vision, FER is a wide range of application in human- computer interaction, behavior, and human expression. However, most of these related researches use general image classification network, which lead inadaptability while applying to face detection. This paper proposed a deep learning using Convolution Neural Network (CNN) with Channel Attention Module. The FER 2013 dataset is utilized in this research for effective classification of face detection. The Data Augmentation is used in this experiment for data pre-processing and feature extraction using high feature generation pyramid (HFGP) and low feature generation pyramid (LFGP). Face detector using Single-Short multi biox detector (SSD) and ResNet 10 based face detection. Then, face detector are given imput to channel attention module classifer which utlized Deep Learning (DL) for effective classification. The obtained result show that the proosed DL using CNN model achieves better accuracy of 99.90% on FER 2013 dataset which ensure accurate classification compared to other existing methods like Engagement Index, Deep Neural Net and Zoning based model.

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Target Tracking Based on STM32 Micro-Controller Based 3-Axis Gimbal Controller

Target Tracking Based on STM32 Micro-Controller Based 3-Axis...

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Range technology (ICORT), international conference on

作者： Ranjeet Kumar Alok Kanti Deb Dept. of Electrical Enginering Indian Institute of Technology Kharagpur Kharagpur West Bengal India

Visual object tracking has become an essential area of research because of its applications in area/boundary surveillance, rescue missions, traffic monitoring, etc. Stabilized image input is an essential component in real-time target tracking. Camera stabilization system (gimbal controller) has been widely used for stabilization. A 3-axis gimbal controller with a USB camera module has been taken to track an object in this work. The gimbal controller hardware has built-in Proportional-Integral-Derivative (PID) controllers to actuate the three motors for roll $(\varphi)$, pitch $(\theta)$, and yaw $(\psi)$ axes. The controller has been tuned for better attitude tracking. An object detector has been used for real-time object detection and obtaining the target offset from the captured image center. These offsets drive the angle commands for the gimbal controller to keep the target at the image center. Robot Operating system has been used as middleware for implementing and coordinating different hardware and software modules.

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Apple Target detection Task in Complex Scenes Based on Yolov5 Method

Apple Target Detection Task in Complex Scenes Based on Yolov...

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Electronic Information Engineering and Computer Communication (EIECC), international conference on

作者： Chunyan Gu Hongjun Ma Cheng Gu Zhewen Huang Teng Fei Sichuan University Jinjiang College Meishan China School of Information Engineering Wuhan University of Technology Wuhan China School of Information Management Wuhan University Wuhan China School of Communication and Information Engineering Xi'an University of Posts and Telecommunications Xi'an China School of Mechanical and Automotive Engineering Xiamen University of Technology Xiamen China

In this paper, an image recognition model based on YOLOv5 and ResNet50 is proposed for the design of image recognition system of fruit picking robot. We first used YOLOv5 to identify and estimate the quantity, location, maturity, and quality of apples, and then used Resnet50-based deep learning models for further classification. Through in-depth analysis and processing of apple image data sets, our model can accurately identify apples, estimate the quantity and quality of apples, and effectively classify the maturity of apples.

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FPGA Based on Voting system along with Cross Voters detection

FPGA Based on Voting System along with Cross Voters Detectio...

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technology, Engineering, Management for Societal impact using Marketing, Entrepreneurship and Talent (TEMSMET), IEEE international conference on

作者： Bandi Raja Babu Jajula Ravi Teja Thorati Som Bhusan Addanaki Janardhan Ashish Thakur Department of Electronics and Communication Engineering Vignan’s Foundation for Science Technology and Research Vadlamudi Guntur Andhra Pradesh India

This article presents the FPGA Based on Voting system along with Cross Voters detection using Python. The proposed system consists of three modules, namely the Vote Monitoring Module (VMM), Image Acquisition Module (IAM) and Image detection Module, for casting and knowing the result of the election, image capturing purpose and Detecting the cross voters using facial recognition respectively. The Voting Unit (VMM) unit is implemented on FPGA(Zed board- ZYNQ 7000) using Verilog in such a way that consists three modules namely Button control module, Vote logger module and mode control. Button control module is used for casting the vote and knowing the result of each candidate. Mode control is used to choose the mode whether it is in voting mode or result mode and vote logger module is used to store the votes of each candidate separately. The Image acquisition module is used to detect the motion of the voter capture his face and vote casting image and that it sends to dedicated server for backup purpose and cross checking purpose. Simultaneously Image detection Module (IDM) unit is used to check the cross voter images if it finds any cross voter face those will be removed and respective votes will treat as invalid votes. Those invalid votes will remove from genuine votes.

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Crime Scene Object detection from Surveillance Video by using Tiny YOLO Algorithm

Crime Scene Object Detection from Surveillance Video by usin...

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Pervasive Computing and Social Networking (ICPCSN), international conference on

作者： Uganya G I. Sudha V. Lakshmanan Finney Daniel Shadrach P. Muthu Krishnammal Nandhini T J Department of ECE Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai India Institute of CSE Saveetha School of Engineering Saveetha Institute of Medical and Technical Science -(SIMATS) Chennai India Department of Information Technology Sri Eshwar College of Engineering Coimbatore India Department of Electronic and Communication Engineering KPR Institute of Engineering and Technology Coimbatore India School of Electronics Engineering VIT-AP University Amaravati Andhrapradesh India Saveetha School of Engineering Saveetha Institute of Medical and Technical Science -(SIMATS) Chennai India

In recent years, law enforcement agencies and security professionals have rewarded much attention to using artificial intelligence for criminal detection based on video surveillance. The ability of deep learning (DL) models to automatically detect and follow prospective offenders saves time and money for law enforcement organization signs by allowing them to understand complicated patterns from data. This helps them conduct in-depth probes and direct their search efforts more precisely. Bladed crimes, such as swords, daggers, knives, and bayonets, and portable firearms, such as pistols, Hand gun or carbines, rifles, Kinfe and machine guns, are often found at crime scenes. In this research, a deep learning based surveillance system is proposed that is capable of identifying the presence of traced objects, such as handguns and knives, and potentially warning authorities of impending danger. Compared to DL-based object identification algorithms like the Enhanced single shot detector (ESSD) ImageNet and FRCNN (Faster Region-based convolutional neural networks), Tiny YOLO has the best real-time detection mean average precision and inference speed. Thus, proposed solution will incorporate YOLO.

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Ship Trajectory Prediction with Social History LSTM

Ship Trajectory Prediction with Social History LSTM

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Computer, Control and Robotics (ICCCR), international conference on

作者： Wenfeng Zhao Xudong Zhang School of Computer and Artificial Intelligence Wuhan University of Technology Wuhan China

Ship trajectory prediction is an important research topic in ship navigation automation. It can effectively help ship pilots to obtain comprehensive Marine traffic information and reduce the potential collision risk of ships. Compared with pedestrian and vehicle trajectory prediction, due to the existence of AIS system for ships, it is easier to obtain historical data of ships, so it is easier to explore the periodicity of trajectories in time. Therefore, based on the previous research on trajectory prediction, we propose a new history module, which adds the agent's long-term route intention to the model to guide the model to predict the ship trajectory. At the same time, we adopt a data-driven idea and pre-train a State Refinement module to obtain high-dimensional feature representations of velocity. Experiments show that our model is superior to the existing current algorithms in long-term trajectory prediction on self-made data sets.

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Enhanced Pneumonia detection Through Cough Analysis Techniques

Enhanced Pneumonia Detection Through Cough Analysis Techniqu...

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international conference on Innovative Mechanisms for Industry Applications (ICIMIA)

作者： A. Ramesh Babu V. Sathvika Abdul Mannan Junaid S. Abhi Ram V.Akhila Reddy K. Shiva Shankar Reddy School of Computer Science & Artificial Intelligence SR University Warangal India

Millions of individuals worldwide are afflicted with pneumonia, a potentially fatal respiratory infection that is an inflammatory disease that affects the air sacs in the lungs. For prompt intervention and successful treatment, pneumonia must be identified early. The proposed approach analyzes cough sounds and recognizes patterns linked to pneumonia by using 1D-CNN and sophisticated signal processing techniques. One promising non-invasive, low-cost method for detecting pneumonia may be cough sound analysis. A plethora of information about the respiratory system can be found in cough sounds, and recent advances in machine learning have made it possible to create accurate and trustworthy frameworks for the detection of pneumonia from cough sounds. Our results propose that cough sound analysis has the potential to be a valuable apparatus for pneumonia detection in resource-limited settings. Our framework is simple to utilize and does not require any specialized equipment, making it perfect for utilize in community health clinics and other inaccessible areas. The algorithm showed 91.35% test accuracy for the 82 cases of cough sound in pneumonia group and 91 cases in non-pneumonia group.

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Role of Artificial intelligence in mode of Diabetic Retinopathy Imaging Techniques: A Review Artificial intelligence in Diabetic Retinopathy

Role of Artificial Intelligence in mode of Diabetic Retinopa...

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international conference on Innovative Sustainable Computational Technologies (CISCT)

作者： Alpana Kumari Vivek Kumar Garg dept. of Optometry Chandigarh University (UIAHS) Mohali India dept. of Medical Lab Technology Chandigarh University (UIAHS) Mohali India

Artificial intelligence (AI), usually referred to as machine intelligence, is a scientific field that gives robots the ability to think like people. AI is a term used to describe a system that simulates human intellect using computer programming. AI is advancing quickly in many multidisciplinary fields, including ophthalmology, from healthcare to the accurate preventive measures, investigation, and treatment plans of illnesses. Because of the emphasis on imaging in the diagnosis of eye illnesses, optometry and ophthalmology is at the forefront of AI in medical field and medicine. Lately, the most prevalent illnesses that cause blindness and visual impairment, such as diabetic retinopathy (DR), have been subjected to deep learning-based AI screening and predictive algorithm models. Deep learning machine with algorithms are the computer models made up of several layers of simulated neurons, are largely responsible for the success of AI in medical field and medicine. These models are capable of learning how data is represented at various levels of abstraction. The trained AI system classified the various forms of DR on optical coherence tomography pictures with accuracy equivalent to that of human specialists. In this study, we focus on the core ideas of AI and how it is applied to the various forms of DR. We also go into further detail on how AI and DL are used in the future of ophthalmology.

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Deep Learning with Histogram of Oriented Gradients- based Computer-Aided Diagnosis for Breast Cancer detection and Classification

Deep Learning with Histogram of Oriented Gradients- based Co...

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Smart Data intelligence (ICSMDI), international conference on

作者： Anitha Ponraj R.Aroul Canessane Sathyabama Institute of Science and Technology Sathyabama University Chennai India

In the modern era, cancer is a major public health concern. Breast cancer is one of the leading causes of death among women. Breast cancer is becoming the top cause of death in women worldwide. Early identification of breast cancer allows patients to receive proper treatment, improving their chances of survival. The proposed Generative Adversarial Networks (GAN) approach is designed to aid in the detection and diagnosis of breast cancer. GANs are deep learning algorithms that generate new data instances that mimic the training data. GAN is made up of two parts: a generator that learns to generate false data and a discriminator that learns from this false data. Furthermore, the histogram of oriented gradients (HOG) is utilized as a feature descriptor in image processing and other computer vision techniques. Gradient orientation in the detection window and region of interest is determined by the histogram of oriented gradients descriptor approach. Using an image dataset and deep learning techniques, the proposed research (GAN-HOG) aims to improve the efficiency and performance of breast cancer diagnosis. The deep learning method is used here to analyze image data by segmenting and classifying the input photographs from the dataset. Unlike many existing nonlinear classification models, the proposed method employs a conditional distribution for the outputs. The proposed model GAN-HOG had an accuracy of 98.435%, a ResNet50 accuracy of 87.826%, a DCNN accuracy of 92.547%, a VGG16 accuracy of 89.453%, and an SVM accuracy of 95.546%.

关键词： Deep learning Support vector machines Histograms Image segmentation Computational modeling Training data Generative adversarial networks

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Research on Information Processing system Under Computer Artificial intelligence Big Data technology

Research on Information Processing System Under Computer Art...

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Signal Processing, Communications and Engineering Management conference (ISPCEM), international

作者： Lei Peng Qiang Fu Zhi Li Musong Lin Yihua Qian Guangdong Key Laboratory of Electric Power Equipment Reliability Electric Power Research Institute of Guangdong Power Grid Co. Ltd. Guangzhou China

In this paper, a virtual view image post-processing method using background information is proposed. Firstly, a depth-based method is used to deal with overlapping between two images. Secondly, the artifacts of the left and right virtual view are located by the motion direction of the observation point, and the artifacts are eliminated by the background pixels in the supplementary view Angle. Then the background image is filled with holes and combined with the foreground image to form a virtual perspective image. The experimental results show that the peak signal noise of this method is 0.6357 dB better than that of the traditional method. Experiments show that the method proposed in this paper can improve the rendering effect significantly.

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