An assistive model of obstacle detection based on deep learning: YOLOv3 for visually impaired people

Rachburee, Nachirat; Punlumjeak, Wattana

doi:10.11591/ijece.v11i4.pp3434-3442

Cited by 12 publications

(11 citation statements)

References 33 publications

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“…These analyses of performance are important, since they present the best model for the effective building of real-world models to assist blinded and visually impaired people. In [10], the authors proposed an assistive application model for visually impaired people based on deep learning, specifically YOLOv3 with a Darknet-53 base network, installed on a smartphone. The model is trained using the Pascal VOC2007 and Pascal VOC2012 datasets and achieves high speed and accuracy in obstacle detection.…”

Section: Related Work In Obstacle Detectionmentioning

confidence: 99%

Investigating YOLO Models Towards Outdoor Obstacle Detection For Visually Impaired People

He,

Saha

2023

Preprint

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The utilization of deep learning-based object detection is an effective approach to assist visually impaired individuals in avoiding obstacles. In this paper, we implemented seven different YOLO object detection models viz., YOLO-NAS (small, medium, large), YOLOv8, YOLOv7, YOLOv6, and YOLOv5 and performed comprehensive evaluation with carefully tuned hyperparameters, to analyze how these models performed on images containing common daily-life objects presented on roads and sidewalks. After a systematic investigation, YOLOv8 was found to be the best model, which reached a precision of 80% and a recall of 68.2% on a well-known Obstacle Dataset which includes images from VOC dataset, COCO dataset, and TT100K dataset along with images collected by the researchers in the field. Despite being the latest model and demonstrating better performance in many other applications, YOLO-NAS was found to be suboptimal for the obstacle detection task.

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Section: Related Work In Obstacle Detectionmentioning

confidence: 99%

Investigating YOLO Models Towards Outdoor Obstacle Detection For Visually Impaired People

He,

Saha

2023

Preprint

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“…On the other hand, the detection speed of these methods is relatively slow, and this is especially the case when they are implemented on low-power processing units like the NVDIA Jetson Nano. The you only look once (YOLO) algorithm [23], [25]- [27] is a novel method that is based on deep learning. It is an enhanced and faster alternative to the traditional approach.…”

Section: Introductionmentioning

confidence: 99%

Face mask detection and counting using you only look once algorithm with Jetson Nano and NVDIA giga texel shader extreme

Al-Selwi

Hassan

Ghani

et al. 2023

IJ-AI

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Deep learning and machine learning are becoming more extensively adopted artificial intelligence techniques for machine vision problems in everyday life, giving rise to new capabilities in every sector of technology. It has a wide range of applications, ranging from autonomous driving to medical and health monitoring. For image detection, the best reported approach is the you only look once (YOLO) algorithm, which is the faster and more accurate version of the convolutional neural network (CNN) algorithm. In the healthcare domain, YOLO can be applied for checking the face mask wearing of the people, especially in a public area or before entering any closed space such as a building to avoid the spread of the air-borne disease such as COVID-19. The main challenges are the image datasets, which are unstructured and may grow large, affecting the accuracy and speed of the detection. Secondly is the portability of the detection devices, which are generally dependent on the more portable like NVDIA Jetson Nano or from the existing computer/laptop. Using the low-power NVDIA Jetson Nano system as well as NVDIA giga texel shader extreme (GTX), this paper aims to design and implement real-time face mask wearing detection using the pre-trained dataset as well as the real-time data.

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“…This system helps in categorizing the traffic flow into different segments for accurate prediction and optimizing the coordination plan. It is also due to the Int J Elec & Comp Eng ISSN: 2088-8708  concentration of vehicles on a specific route so that it is easy to introduce a better traffic control mechanism [1]. For instance, a detection algorithm is used for the précised regulation of road traffic.…”

Section: Introductionmentioning

confidence: 99%

Application of improved you only look once model in road traffic monitoring system

Kamble

Kounte

2023

IJECE

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<span lang="EN-US">The present research focuses on developing an intelligent traffic management solution for tracking the vehicles on roads. Our proposed work focuses on a much better you only look once (YOLOv4) traffic monitoring system that uses the CSPDarknet53 architecture as its foundation. Deep-sort learning methodology for vehicle multi-target detection from traffic video is also part of our research study. We have included features like the Kalman filter, which estimates unknown objects and can track moving targets. Hungarian techniques identify the correct frame for the object. We are using enhanced object detection network design and new data augmentation techniques with YOLOv4, which ultimately aids in traffic monitoring. Until recently, object identification models could either perform quickly or draw conclusions quickly. This was a big improvement, as YOLOv4 has an astoundingly good performance for a very high frames per second (FPS). The current study is focused on developing an intelligent video surveillance-based vehicle tracking system that tracks the vehicles using a neural network, image-based tracking, and YOLOv4. Real video sequences of road traffic are used to test the effectiveness of the method that has been suggested in the research. Through simulations, it is demonstrated that the suggested technique significantly increases graphics processing unit (GPU) speed and FSP as compared to baseline algorithms.</span>

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An assistive model of obstacle detection based on deep learning: YOLOv3 for visually impaired people

Cited by 12 publications

References 33 publications

Investigating YOLO Models Towards Outdoor Obstacle Detection For Visually Impaired People

Investigating YOLO Models Towards Outdoor Obstacle Detection For Visually Impaired People

Face mask detection and counting using you only look once algorithm with Jetson Nano and NVDIA giga texel shader extreme

Application of improved you only look once model in road traffic monitoring system

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