Weapon Detection Rope Roaming Human Safety Robot

Maduri, Praveen Kumar; Saini, Himanshu; Tripathi, Pradyuman Mani; Singh, Sakshi

doi:10.1007/978-981-19-1618-2_5

Cited by 2 publications

(2 citation statements)

References 4 publications

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“…The application of military IoT could be found in the field of battlefield perception, improving the early warning, weapons and equipment management, intelligence sharing ability and support efficiency of the military, logistics support, military training and so on [50,51]. To be more specific, military IoT studies also consider WDS, such as rope roaming robots for 360 degrees of monitoring a specific area [52], a semi-autonomous robot with WDS and stair climbing functions [53], drones or Unmanned Aerial Vehicles (UAV) for WDS [54] and explosive weapons detector [55]. To partially solve the issue associated with high power consumption, it was advised [56] to use Field Programmable Gate Array (FPGA).…”

Section: Related Workmentioning

confidence: 99%

Weapons Detection System Based on Edge Computing and Computer Vision

Burnayev¹,

Toibazarov²,

Atanov³

et al. 2023

IJACSA

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Early detection of armed threats is crucial in reducing accidents and deaths resulting from armed conflicts and terrorist attacks. The most significant application of weapon detection systems would be found in public areas such as airports, stadiums, central squares, and on the battlefield in urban or rural conditions. Modern surveillance and control systems of closed-circuit television cameras apply deep learning and machine learning algorithms for weapons detection on the base of cloud architecture. However, cloud computing is inefficient for network bandwidth, data privacy and slow decision-making. To address these issues, edge computing can be applied, using Raspberry Pi as an edge device with the EfficientDet model for developing the weapons detection system. The image processing results are transmitted as a text report to the cloud platform for further analysis by the operator. Soldiers can equip themselves with the suggested edge node and headphones for armed threat notifications, plugged into augmented reality glasses for visual data output. As a result, the application of edge computing makes it possible to ensure data safety, increase the network bandwidth and provide the device operation without the internet. Thus, an independent weapon detection system was developed that identifies weapons in 1.48 seconds without the Internet.

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Section: Related Workmentioning

confidence: 99%

Weapons Detection System Based on Edge Computing and Computer Vision

Burnayev¹,

Toibazarov²,

Atanov³

et al. 2023

IJACSA

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“…Deep learning is growing fast not only in other computer vision related tasks [2][3][4][5], but also in image generation. In the initial research, GAN, a framework for generating models through confrontation process estimation, was proposed [6].…”

Section: Introductionmentioning

confidence: 99%

The study of object transformation based on CycleGAN

Wang

2023

Fifth International Conference on Computer Information Science and Artificial Intelligence (CISAI 2022)

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At present, generative adaptive networks (GAN), has become a popular module in deep learning. GAN is very effective in image generation and image style migration. At present, the research on the migration of image style is focused on images such as oil painting and landscape painting, and lacks the research on the conversion between object images. This paper extends the style transfer technology to object recognition, uses CycleGAN method to learn the mapping relationship between zebra and horse, and realizes the transformation between zebra and horse. Viewing the generation effect of different learning methods by changing the learning times and learning rate policy. This work realizes the conversion between zebra and horse, and shows the generated pictures under different training times and different learning situations. Under the same training times, the conversion effect from horse to zebra will be better. After a certain number of trainings, the training effect will gradually decline. The conversion effect of the same type will be improved with the increase of training times. Different learning rate policies will bring different generation effects.

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Weapon Detection Rope Roaming Human Safety Robot

Cited by 2 publications

References 4 publications

Weapons Detection System Based on Edge Computing and Computer Vision

Weapons Detection System Based on Edge Computing and Computer Vision

The study of object transformation based on CycleGAN

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