Model for Real-Time Object Searching and Recognizing on Mobile Platform

Kushnir, Dmytro; Paramud, Yaroslav

doi:10.1109/tcset49122.2020.235407

Cited by 2 publications

(1 citation statement)

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“…It generates the final output, which includes classes, objectness scores, and bounding boxes, by using anchor boxes on feature maps. The improved ASPP (Atrous Spatial Pyramid Pooling) module is used to replace the SPP (Spatial Pyramid Pooling) module of the original YOLOv5s algorithm to obtain the receptive fields of images of various sizes, allowing for better context capture [8]. This software can even further be extended to vehicle and pedestrian detection [9].…”

Section: Fig 3cascade Classification Eyolov5 Algorithmmentioning

confidence: 99%

Smart Assistive System for the Visually Impaired

Mejalin Arno B,

Vinothagan J,

Arun Kumar R

et al. 2023

IJETMS

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Recent advances in innovation have made ordinary people's lives richer, simpler, and easier. Many people suffer from vision impairment, which causes many difficulties in their daily work, according to World Health Organization (WHO) statistics. Our goal is to develop a framework that is unobtrusive, safe, portable, and versatile in order to assist blind people in their daily lives. It intends to develop an effective system for assisting the visually impaired with obstacle detection and scene classification. The Raspberry-Pi 3B+ and a camera module are used in the proposed method. It photographs the scene and then preprocesses the images with Viola-Jones and Yolov5 object detection algorithms. The aforementioned techniques are used to detect people and objects. We also used a sound system that speaks when an object or person is detected. The presented study implements simple computations and detects obstacles with high efficiency. Unlike other frameworks, this framework is lightweight and easy to transport

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Section: Fig 3cascade Classification Eyolov5 Algorithmmentioning

confidence: 99%

Smart Assistive System for the Visually Impaired

Mejalin Arno B,

Vinothagan J,

Arun Kumar R

et al. 2023

IJETMS

View full text Add to dashboard Cite

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Methods and means for real-time object recognition accuracy increase in video images on ios mobile platform

Kushnir¹

2021

СSN

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As a result of the analytical review, it was established that the family of Yolo models is a promising area of search and recognition of objects. However, existing implementations do not support the ability to run the model on the iOS platform. To achieve these goals, a comprehensive scalable conversion system has been developed to improve the recognition accuracy of arbitrary models based on the Docker system. The method of improvement is to add a layer with the Mish activation function to the original model. The method of conversion is to quickly convert any Yolo model to CoreML format. As part of the study of these techniques, a model of the neural network Yolov4_TCAR was created. Additionally, a method of accelerating the load on the CPU using an additional layer of neural network with the function of activating Mish in Swift for the iOS mobile platform was added. As a result, the effectiveness of the Mish activation function, the CPU load of the mobile device, the amount of RAM used, and the frame rate when using the improved original Yolov4-TCAR model were studied. The results of the research confirmed the functioning of the algorithm for conversion and accuracy increase of the neural network model in real-time.

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Model for Real-Time Object Searching and Recognizing on Mobile Platform

Cited by 2 publications

References 0 publications

Smart Assistive System for the Visually Impaired

Smart Assistive System for the Visually Impaired

Methods and means for real-time object recognition accuracy increase in video images on ios mobile platform

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