Aerial image detection and recognition system based on deep neural network

Health care for independently living elders is more important than ever. Automatic recognition of their Activities of Daily Living (ADL) is the first step to solving the health care issues faced by seniors in an efficient way. The paper describes a Deep Neural Network (DNN)-based recognition system aimed at facilitating smart care, which combines ADL recognition, image/video processing, movement calculation, and DNN. An algorithm is developed for processing skeletal data, filtering noise, and pattern recognition for identification of the 10 most common ADL including standing, bending, squatting, sitting, eating, hand holding, hand raising, sitting plus drinking, standing plus drinking, and falling. The evaluation results show that this DNN-based system is suitable method for dealing with ADL recognition with an accuracy rate of over 95%. The findings support the feasibility of this system that is efficient enough for both practical and academic applications.

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Real-time Hand-Gesture Recognition based on Deep Neural Network

SHS Web of Conferences ◽

10.1051/shsconf/202110204009 ◽

2021 ◽

Vol 102 ◽

pp. 04009

Author(s):

Naoto Ageishi ◽

Fukuchi Tomohide ◽

Abderazek Ben Abdallah

Keyword(s):

Neural Network ◽

Real Time ◽

Gesture Recognition ◽

Deep Neural Network ◽

Recognition System ◽

Hand Gesture Recognition ◽

Hand Gesture ◽

Robotic Control ◽

Driver Assistance Systems ◽

Fast Classification

Hand gestures are a kind of nonverbal communication in which visible bodily actions are used to communicate important messages. Recently, hand gesture recognition has received significant attention from the research community for various applications, including advanced driver assistance systems, prosthetic, and robotic control. Therefore, accurate and fast classification of hand gesture is required. In this research, we created a deep neural network as the first step to develop a real-time camera-only hand gesture recognition system without electroencephalogram (EEG) signals. We present the system software architecture in a fair amount of details. The proposed system was able to recognize hand signs with an accuracy of 97.31%.

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Fully-automatic natural plant recognition system using deep neural network for dynamic outdoor environments

SN Applied Sciences ◽

10.1007/s42452-019-0785-9 ◽

2019 ◽

Vol 1 (7) ◽

Cited By ~ 1

Author(s):

Masoud Fathi Kazerouni ◽

Nazeer T. Mohammed Saeed ◽

Klaus-Dieter Kuhnert

Keyword(s):

Neural Network ◽

Deep Neural Network ◽

Recognition System ◽

Natural Plant ◽

Plant Recognition ◽

Outdoor Environments ◽

Fully Automatic

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E-Commerce Picture Text Recognition Information System Based on Deep Learning

Computational Intelligence and Neuroscience ◽

10.1155/2022/9474245 ◽

2022 ◽

Vol 2022 ◽

pp. 1-11

Author(s):

Bin Zhao ◽

WenYing Li ◽

Qian Guo ◽

RongRong Song

Keyword(s):

Neural Network ◽

Target Detection ◽

Recognition Accuracy ◽

Text Recognition ◽

Detection Accuracy ◽

Image Detection ◽

Evaluation Indexes ◽

Different Levels ◽

Made In ◽

Detection And Recognition

For the accuracy requirements of commodity image detection and classification, the FPN network is improved by DPFM ablation and RFM, so as to improve the detection accuracy of commodities by the network. At the same time, in view of the narrowing of channels in the application of traditional MWI-DenseNet network, a new GTNet network is proposed to improve the classification accuracy of commodities.The results show that at different levels of evaluation indexes, the dpFPN-Netv2 algorithm improved by DPFM + RFM fusion has higher target detection accuracy than RetinaNet-50 algorithm and other algorithms. And the detection time is 52 ms, which is significantly lower than 90 ms required for RetinaNet-50 detection. In terms of target recognition, compared with the traditional MWI-DenseNet neural network, the computation amount of the improved MWI DenseNet neural network is significantly reduced under different shunt ratios, and the recognition accuracy is significantly improved. The innovation of this study lies in improving the algorithm from the perspective of target detection and recognition, so as to change the previous improvement that only can be made in a single way.

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Detection of Vehicular Traffic Using Convolutional Neural Networks

International Journal of Innovative Technology and Exploring Engineering - Special Issue ◽

10.35940/ijitee.a1008.0881019 ◽

2019 ◽

Vol 8 (10) ◽

pp. 1423-1426

Keyword(s):

Neural Network ◽

Convolutional Neural Network ◽

Recognition System ◽

Aerial Images ◽

Aerial Image ◽

Detection Methods ◽

Design System ◽

Detection Process ◽

Present Generation ◽

Successful Design

— In present generation the detection of vehicle using aerial images plays an important role and mot challenging. The video understanding, border security are the applications of aerial images. To improve the performance of the system different detection methods are introduced. But these methods take more time in detection process. To overcome these convolutional neural network are introduced which will produce the successful design system. the main intent of this paper is to present the recognition system for aerial images using convolutional neural network. The proposed method improves the accuracy and speed after the detection process. At last aerial image is obtained by matching the image and textual description of classes.

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Smartphone assist deep neural network model to recognize the high-quality tea using leaf maturity and its effect on leaf chlorophyll

Journal of Applied and Natural Science ◽

10.31018/jans.v13i4.2950 ◽

2021 ◽

Vol 13 (4) ◽

pp. 1249-1255

Author(s):

Utpal Barman ◽

Ridip Dev Choudhury ◽

Bipul Kumar Talukdar ◽

George Bhokta ◽

Sahrul Alom Choudhari ◽

...

Keyword(s):

Neural Network ◽

Deep Neural Network ◽

Nearest Neighbor ◽

Recognition System ◽

Support Vector ◽

K Nearest Neighbor ◽

Tea Leaves ◽

High Quality ◽

Leaf Maturity ◽

Tea Leaf

Immature and tender tea leaves always produce high-quality tea than mature tea leaves. Depending on the maturity and age of the leaf, the colour and texture of the tea leaf are different. The photosynthesis capacity of the tea leaf also changes with the change of leaf maturity. Though the tea farmer plucks, classifies, and recognizes the best tea leaves (immature and tender) by viewing the visual symptoms and position of the leaves, the method is not authentic all time and leads to the overall degradation of the tea quality. The present study presents a smartphone assist tea leaf recognition system by analyzing the colour and texture properties of the tea leaf. The six different colour features and 4 Haralick texture features were extracted in the colour and grey domain of the leaf images. Three types of tea leaves, i.e., mature, immature, and tender, were classified using Deep Neural Network (DNN) with ADAM (Adaptive Moment Estimation) optimizer. With an accuracy of 97%, the DNN outperformed the Support Vector Machine (SVM) and K Nearest Neighbor (KNN). The SVM and KNN reported a total of 94.42% and 95.53% accuracy, respectively. The investigated system using DNN with an average precision and recall value of 98.67 and 98.34, respectively, may detect and classify the tea leaf maturity status. The system also can be used in AI-based tea plucking robotic systems or machines.

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