检索结果-内蒙古大学图书馆

A Survey on Wild Creatures Alert System to Protect Agriculture Lands Domestic Creatures and People 1

2nd International Conference on Ubiquitous Computing and Intelligent Information Systems (ICUIS)

作者： Makanyadevi, K. Aarthi, M. Kavyadharsini, P. Keerthika, S. Sabitha, M. M Kumarasamy Coll Engn Dept Comp Sci & Engn Karur 639113 Tamil Nadu India

ISBN: (数字)9789811925412

ISBN: (纸本)9789811925412;9789811925405

Wild animal ready framework in ML is utilized to secure the property, fringe, and runway from wild creatures with enormous information study of a hazardous wild monster. Checking the exhibition of wild animals is utilized to secure the farmlands. In old ways of spotting creatures in paddy fields and residences embody crafted by regular eyes to notice the creature movement. It is not feasible for men to watch creature developments ceaselessly for the duration of the day. From where a desire of specific discovery of animals which penetrate appreciably in the rice fields and the agricultural lands of the individuals. The ways utilized for the prevalence of the creatures encapsulate division and item discovery strategies. This result requires difficulties in the development of returns and causes the ranch failure. This tosses back to ranchers destroying the land on account of incessant monstrosities by wild animals.

关键词： Bounding box Dataset Deep learning Machine learning Object detection yolo algorithm

来源：评论

学校读者我要写书评

暂无评论

Custom Face Recognition Using yolo.V3 3

Custom Face Recognition Using YOLO.V3

引用

3rd International Conference on Signal Processing and Communication (ICPSC)

作者： Menon, Suman M. Geroge, Anju Aswathy, N. James, Jaimy Adi Shankara Inst Engn & Technol VLSI & Embedded Syst Kalady India

ISBN: (纸本)9781665428644

Face recognition technique is adopted in many applications such as surveillance systems, medical field, security, robot navigation, etc. Today because of deep learning there is a large improvement in the area of face recognition. The importance of facial recognition comes to more importance at the time of a pandemic situation, for attendance marking people monitoring, in health care, social distancing etc. Many facial recognition algorithms are available today. But the performance of these algorithms lacks many factors and which lead to poor performance in real-time applications. The most commonly used algorithms for face recognition are R-CNN, Fast R-CNN, etc. The main problems with these algorithms are they have poor processing speed;they take time to produce the output. To overcome this problem, yolo.V3 algorithm can be used for facial recognition which produces faster output. In this paper we will study on different versions of yolo algorithms, study on existing algorithm for face detection and recognition (RCNN),and conclude with the result obtained for the yolo.V3 algorithm for recognizing a custom face.

关键词： Face recognition processing speed yolo algorithm R-CNN

来源：评论

学校读者我要写书评

暂无评论

yolov7oSAR: A Lightweight High-Precision Ship Detection Model for SAR Images Based on the yolov7 algorithm

引用

REMOTE SENSING 2024年第5期16卷 913页

作者： Liu, Yilin Ma, Yong Chen, Fu Shang, Erping Yao, Wutao Zhang, Shuyan Yang, Jin Chinese Acad Sci Aerosp Informat Res Inst Beijing 100094 Peoples R China Univ Chinese Acad Sci Coll Resources & Environm Beijing 100049 Peoples R China

Researchers have explored various methods to fully exploit the all-weather characteristics of Synthetic aperture radar (SAR) images to achieve high-precision, real-time, computationally efficient, and easily deployable ship target detection models. These methods include Constant False Alarm Rate (CFAR) algorithms and deep learning approaches such as RCNN, yolo, and SSD, among others. While these methods outperform traditional algorithms in SAR ship detection, challenges still exist in handling the arbitrary ship distributions and small target features in SAR remote sensing images. Existing models are complex, with a large number of parameters, hindering effective deployment. This paper introduces a yolov7 oriented bounding box SAR ship detection model (yolov7oSAR). The model employs a rotation box detection mechanism, uses the KLD loss function to enhance accuracy, and introduces a Bi-former attention mechanism to improve small target detection. By redesigning the network's width and depth and incorporating a lightweight P-ELAN structure, the model effectively reduces its size and computational requirements. The proposed model achieves high-precision detection results on the public RSDD dataset (94.8% offshore, 66.6% nearshore), and its generalization ability is validated on a custom dataset (94.2% overall detection accuracy).

关键词： yolo algorithm ship detection oriented bounding box Synthetic aperture radar (SAR) images

来源：评论

学校读者我要写书评

暂无评论

A Deep Learning-based Approach on Vehicle Detection, Tracking, and Counting System 5

A Deep Learning-based Approach on Vehicle Detection, Trackin...

引用

5th International Conference on Inventive Computation Technologies (ICICT)

作者： Rajavinu, N. Swaminathan, B. Shameer, A. P. Jagadish, Dwaitha Ghosh, Debarati Chanda, Pramit Brata SA Engn Coll Dept Elect & Elect Engn Chennai Tamil Nadu India Saveetha Sch Engn Dept Comp Sci & Engn Chennai Tamil Nadu India Nam Coll Kallikkandy Dept Comp Sci Kannur Kerala India JAIN Dept Civil Engn Fac Engn & Technol Bengaluru Karnataka India Vidyalankar Inst Technol Dept Informat Technol Mumbai Maharashtra India Kalyani Govt Engn Coll Dept Comp Sci & Engn Kalyani W Bengal India

ISBN: (数字)9781665408370

ISBN: (纸本)9781665408387;9781665408370

Transportation is one of the major features of civilization. Ever since the human race evolved, the need to move from one place to another has become mandatory. Currently, there are a lot of ways of transportation available. Yet, most of the population prefer roadways as it is simpler and more convenient. Due to the alarming increase in population, the number of vehicles on the road has also increased. Thus, it becomes tougher for the traffic police and the government to monitor the movement of all vehicles at different places. To resolve this issue, this study aims in finding the best Deep Learning (DL) algorithm that can be used to detect, track and count the number of vehicles from a video. For this process, a dataset consisting of 47 mp3 videos of the road with or without vehicles in every frame is collected from Kaggle. The dataset is then preprocessed. The preprocessing includes the conversion of video to frame-by-frame images. Two DL models will be developed using two different algorithms. The algorithms include the faster RCNN algorithm and the yolo algorithm. The models are then trained using the images taken from the videos. The trained models are tested by analyzing various factors. The factors include precision, recall and the average IoU. The values are also compared in the form of a graph for better results. The performance of both the models is recorded and examined to find that the yolo algorithm is a better algorithm than the faster RCNN when it comes to vehicle detection and tracking.

关键词： Vehicle detection DL data collection faster RCNN algorithm yolo algorithm correlation filter

来源：评论

学校读者我要写书评

暂无评论

Analysis of Classroom Teacher-Student Interaction based on yolo

International Core Journal of Engineering

引用

International Core Journal of Engineering 2022年第5期8卷 194-201页

作者： Chenyu Bai Ziyuan Feng Mengyuan Yang

The classroom behavior of teachers and students is an important basis for classroom teaching evaluation. Classroom behavior analysis aims to study the internal mechanism of teachers'teaching activities and students' academic development in the classroom, to help teachers and students reflect on their own classroom performance, and to promote the improvement of classroom teaching quality. With the continuous deepening of the integration of artificial intelligence technology and education, it is of great significance to change the previous methods of teacher-student classroom behavior analysis for classroom *** present, the field of Intelligent Teaching is in a fast developing period. Thanks to the continuous progress of deep learning algorithms in recent years, the method of classroom teaching evaluation based on video image processing has also emerged. Following the trend of informationization and intellectualization reform in the field of education, this project intends to build an intelligent evaluation system for teacher-student interaction behavior in classroom teaching based on yolo algorithm for object recognition and detection.

关键词： Intelligent Evaluation yolo algorithm Deep Learning Target Detection Computer Vision

来源：评论

学校读者我要写书评

暂无评论

A grading method for Kayser Fleischer ring images based on ResNet

引用

HELIYON 2023年第5期9卷

作者： Song, Wei Xin, Ling Wang, Jiemei Anhui Univ Chinese Med Affiliated Hosp 1 Hefei 230031 Peoples R China Anhui Univ Chinese Med Dept Otolaryngol Affiliated Hosp 1 Hefei 230031 Peoples R China

The corneal K-F ring is the most common ophthalmic manifestation of WD patients. Early diagnosis and treatment have an important impact on the patient's condition. K-F ring is one of the gold standards for the diagnosis of WD disease. Therefore, this paper mainly focused on the detection and grading of the K-F ring. The aim of this study is three-fold. Firstly, to create a meaningful database, the K-F ring images are collected which contains 1850 images with 399 different WD patients, and then this paper uses the chi-square test and Friedman test to analyze the statistical significance. Subsequently, the all collected images were graded and labeled with an appropriate treatment approach, as a result, these images could be used to detect the corneal through the yolo. After the detection of corneal, image segmentation was realized in batches. Finally, in this paper, different deep convolutional neural networks (VGG, ResNet, and DenseNet) were used to realize the grading of the K-F ring images in the KFID. Experimental results reveal that the entire pre-trained models obtain excellent performance. The global accuracies achieved by the six models i.e., VGG-16, VGG-19, ResNet18, ResNet34, ResNet50, and DenseNet are 89.88%, 91.89%, 94.18%, 95.31%, 93.59%, and 94.58% respectively. ResNet34 displayed the highest recall, specificity, and F1-score of 95.23%, 96.99%, and 95.23%. DenseNet showed the best precision of 95.66%. As such, the findings are encouraging, demonstrating the effectiveness of ResNet in the automatic grading of the K-F ring. Moreover, it provides effective help for the clinical diagnosis of HLD.

关键词： HLD Wilson's disease yolo algorithm K-F ResNet

来源：评论

学校读者我要写书评

暂无评论

AI Education Based on Evaluating Concentration of Students in Class 5

AI Education Based on Evaluating Concentration of Students i...

引用

5th International Conference on Video and Image Processing (ICVIP)

作者： Zhang, Yuan Qiu, Changzhen Zhong, Ningze Su, Xieyang Zhang, Xuanrui Huang, Fuquan Wang, Luping Wang, Liang Sun Yat Sen Univ Sch Elect & Commun Engn Shenzhen Peoples R China Sun Yat Sen Univ Sch Mat Sci & Engn Shenzhen Peoples R China New York Univ Tandon Sch Engn New York NY USA South China Normal Univ Neurosci & Artificial Intelligence Moral Learning Guangzhou Peoples R China

ISBN: (纸本)9781450385893

Attention is an indispensable part of the learning process. Currently, teachers in most of the schools judge student' concentration only by their eyes subjectively. But this approach not only distracts teachers from the lesson but also difficult to obtain feedback on the result after class. In this paper, we introduce a method based on machine vision to evaluate students' concentration. We apply the yolo algorithm on facial movement detection to predict whether students are engaged in class. It not only helps to differentiate instruction for students but also helps teachers to optimize their lessons. The experiments demonstrate that the method can achieve the assessment of students' concentration within a small-size classroom.

关键词： Machine Vision Object Detection Students' Concentration yolo algorithm

来源：评论

学校读者我要写书评

暂无评论

A Methodology for Early Detection of Plant Diseases Using Real Time Object Detection algorithm 1

引用

1st International Conference on Smart and Sustainable Agriculture (SSA)

作者： Walia, Ranjeet Sharma, Shivam Shrivastava, Swapnil Natl Inst Technol Anu 177005 Himachal Prades India Ctr Dev Adv Comp CDAC 1 Old Madras Rd Bangalore 560038 Karnataka India

ISBN: (数字)9783030882594

ISBN: (纸本)9783030882594;9783030882587

The convergence of Big Data Analytics, Deep Learning, Internet of Things (IoT) and Cloud Computing has catalyzed the emergence of novel applications in various domains. The growing need for Sustainable Agriculture and advancement in these emerging technologies has enabled applications like Precision Agriculture and Smart Farming. There are several such initiatives where agricultural data is being collected continuously by means of devices (e.g., sensors, cameras) and stored in a suitable Big Data Analytics platform. There is a need to generate actionable insights in real time for these applications using various analytical techniques including Deep Learning. Various techniques have been used for plant disease detection, pest detection, plant growth assessment, yield prediction and so on. As per the Food and Agriculture Organization of the United Nations, there is a growth in frequency of occurrences and complexity of plant diseases. In this work we have attempted to build a contemporary model with available resources for early detection of plant diseases. The freely available plant images dataset was collected from various sources followed by required pre-processing to prepare a large, assorted and robust plant disease dataset. The real time object detection algorithm is applied for efficient and timely plant disease detection. The detailed methodology for creation of material, building the detection model and inferring the result is discussed in this paper. The early detection of plant diseases would help in quick diagnosis and provisioning of remedial measures to the farmers for enabling timely preventive or curative actions.

关键词： Plant disease detection yolo algorithm Deep learning

来源：评论

学校读者我要写书评

暂无评论

Computer Vision-Based Path Planning for Robot Arms in Three-Dimensional Workspaces Using Q-Learning and Neural Networks

引用

SENSORS 2022年第5期22卷 1697-1697页

作者： Abdi, Ali Ranjbar, Mohammad Hassan Park, Ju Hong Pohang Univ Sci & Technol POSTECH Dept Convergence IT Engn Pohang 37673 South Korea Univ Tehran Sch Mech Engn Coll Engn Tehran 111554563 Iran

Computer vision-based path planning can play a crucial role in numerous technologically driven smart applications. Although various path planning methods have been proposed, limitations, such as unreliable three-dimensional (3D) localization of objects in a workspace, time-consuming computational processes, and limited two-dimensional workspaces, remain. Studies to address these problems have achieved some success, but many of these problems persist. Therefore, in this study, which is an extension of our previous paper, a novel path planning approach that combined computer vision, Q-learning, and neural networks was developed to overcome these limitations. The proposed computer vision-neural network algorithm was fed by two images from two views to obtain accurate spatial coordinates of objects in real time. Next, Q-learning was used to determine a sequence of simple actions: up, down, left, right, backward, and forward, from the start point to the target point in a 3D workspace. Finally, a trained neural network was used to determine a sequence of joint angles according to the identified actions. Simulation and experimental test results revealed that the proposed combination of 3D object detection, an agent-environment interaction in the Q-learning phase, and simple joint angle computation by trained neural networks considerably alleviated the limitations of previous studies.

关键词： path planning Q-learning neural network yolo algorithm computer vision robot arm target reaching obstacle avoidance

来源：评论

学校读者我要写书评

暂无评论

Integration of Deep Learning Network and Robot Arm System for Rim Defect Inspection Application

引用

SENSORS 2022年第10期22卷 3927-3927页

作者： Mao, Wei-Lung Chiu, Yu-Ying Lin, Bing-Hong Wang, Chun-Chi Wu, Yi-Ting You, Cheng-Yu Chien, Ying-Ren Natl Yunlin Univ Sci & Technol Grad Sch Engn Dept Elect Engn Touliu 640301 Yunlin Taiwan Natl Ilan Univ Dept Elect Engn Yilan 260007 Taiwan

Automated inspection has proven to be the most effective approach to maintaining quality in industrial-scale manufacturing. This study employed the eye-in-hand architecture in conjunction with deep learning and convolutional neural networks to automate the detection of defects in forged aluminum rims for electric vehicles. RobotStudio software was used to simulate the environment and path trajectory for a camera installed on an ABB robot arm to capture 3D images of the rims. Four types of surface defects were examined: (1) dirt spots, (2) paint stains, (3) scratches, and (4) dents. Generative adversarial network (GAN) and deep convolutional generative adversarial networks (DCGAN) were used to generate additional images to expand the depth of the training dataset. We also developed a graphical user interface and software system to mark patterns associated with defects in the images. The defect detection algorithm based on yolo algorithms made it possible to obtain results more quickly and with higher mean average precision (mAP) than that of existing methods. Experiment results demonstrated the accuracy and efficiency of the proposed system. Our developed system has been shown to be a helpful rim defective detection system for industrial applications.

关键词： robotic arm rim defect detection yolo algorithm deep convolutional generative adversarial networks (DCGAN)

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：