Detection and Recognition of Handguns in the Surveillance Videos using Neural Network

The paper puts forward a real time traffic sign sensing (detection and recognition) frame work for enhancing the vehicles capability in order to have a save driving, path planning. The proposed method utilizes the capsules neural network that outperforms the convolutional neural network by eluding the necessities for the manual effort. The capsules network provides a better resistance for the spatial variance and the high reliability in the sensing of the traffic sign compared to the convolutional network. The evaluation of the capsule network with the Indian traffic data set shows a 15% higher accuracy when compared with the CNN and the RNN.

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License Plate Detection and Recognition using Vertical based Edge Detection Algorithm and Radial Basis Function Neural Network

Indian Journal of Science and Technology ◽

10.17485/ijst/2015/v8i26/80746 ◽

2015 ◽

Vol 8 (26) ◽

Author(s):

P. I. Reji ◽

V. S. Dharun

Keyword(s):

Neural Network ◽

Edge Detection ◽

Radial Basis Function ◽

Basis Function ◽

Detection Algorithm ◽

License Plate ◽

Edge Detection Algorithm ◽

Radial Basis ◽

License Plate Detection ◽

Detection And Recognition

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Moving Human Detection in Surveillance Videos by Parts Matching Approach using Adaptive Segmentation, Multiple Features Set and FF Neural Network

INTERNATIONAL JOURNAL ON Advances in Information Sciences and Service Sciences ◽

10.4156/aiss.vol4.issue21.81 ◽

2012 ◽

Vol 4 (21) ◽

pp. 638-645

Author(s):

Nasir Saleem ◽

Bie Hongxia ◽

Faisal Zafar ◽

Amjad Ali ◽

Tariq Aziz

Keyword(s):

Neural Network ◽

Human Detection ◽

Adaptive Segmentation ◽

Multiple Features ◽

Surveillance Videos

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MorseNet: A Unified Neural Network for Morse Detection and Recognition in Spectrogram

IEEE Access ◽

10.1109/access.2020.3020860 ◽

2020 ◽

Vol 8 ◽

pp. 161005-161017

Author(s):

Weihao Li ◽

Keren Wang ◽

Ling You

Keyword(s):

Neural Network ◽

Detection And Recognition

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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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A NOVEL METHOD FOR PROTECTIVE FACE MASK DETECTION USING CONVOLUTIONAL NEURAL NETWORKS AND IMAGE HISTOGRAMS

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ◽

10.5194/isprs-archives-xliv-2-w1-2021-177-2021 ◽

2021 ◽

Vol XLIV-2/W1-2021 ◽

pp. 177-182

Author(s):

E. Ryumina ◽

D. Ryumin ◽

D. Ivanko ◽

A. Karpov

Keyword(s):

Neural Network ◽

Machine Learning ◽

Hybrid Method ◽

Face Mask ◽

Corpus Analysis ◽

Visual Features ◽

Pixel Intensity ◽

Novel Method ◽

Protective Mask ◽

Detection And Recognition

Abstract. This paper proposes a new hybrid method for automatic detection and recognition of the presence/absence of a protective mask on human's face. It combines visual features extracted using Convolutional Neural Network (CNN) with image histograms that convey information about pixel intensity. Several pre-trained models for building feature extraction systems using a CNN and several types of image histograms are considered in this paper. We test our approach on the Medical Mask Dataset and perform cross-corpus analysis on two other databases named Masked Faces (MAFA) and Real-World Masked Face Dataset (RMFD). We demonstrate that the proposed hybrid method increases the Unweighted Average Recalls (UARs) of recognition of the presence/absence of a protective mask on human's face in comparison with traditional CNNs on the MAFA and RMFD databases by 0.96% and 1.32%, respectively. The proposed method can be generalized and used for other tasks of biometry, computer vision, machine learning and automatic face recognition.

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