A Framework for the Generation of Obstacle Data for the Study of Obstacle Detection by Ultrasonic Sensors

Singh, Bhupendra; Kapoor, Monit

doi:10.1109/jsen.2021.3055515

Cited by 15 publications

(3 citation statements)

References 27 publications

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“…In an emergency case, vibration, buzzer sound, and location alarm are sent to the guardian through their mobile phones. In another recent work, a method for detecting low-lying obstacles was proposed [ 13 ]. In that work, an assistive cane was used with ultrasonic sensors (US) attached to it and facing the forward direction.…”

Section: Related Workmentioning

confidence: 99%

An IoT Machine Learning-Based Mobile Sensors Unit for Visually Impaired People

Dhou

Nabulsi

Al-Ali

et al. 2022

Sensors

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Visually impaired people face many challenges that limit their ability to perform daily tasks and interact with the surrounding world. Navigating around places is one of the biggest challenges that face visually impaired people, especially those with complete loss of vision. As the Internet of Things (IoT) concept starts to play a major role in smart cities applications, visually impaired people can be one of the benefitted clients. In this paper, we propose a smart IoT-based mobile sensors unit that can be attached to an off-the-shelf cane, hereafter a smart cane, to facilitate independent movement for visually impaired people. The proposed mobile sensors unit consists of a six-axis accelerometer/gyro, ultrasonic sensors, GPS sensor, cameras, a digital motion processor and a single credit-card-sized single-board microcomputer. The unit is used to collect information about the cane user and the surrounding obstacles while on the move. An embedded machine learning algorithm is developed and stored in the microcomputer memory to identify the detected obstacles and alarm the user about their nature. In addition, in case of emergencies such as a cane fall, the unit alerts the cane user and their guardian. Moreover, a mobile application is developed to be used by the guardian to track the cane user via Google Maps using a mobile handset to ensure safety. To validate the system, a prototype was developed and tested.

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Section: Related Workmentioning

confidence: 99%

An IoT Machine Learning-Based Mobile Sensors Unit for Visually Impaired People

Dhou

Nabulsi

Al-Ali

et al. 2022

Sensors

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“…Some measures are considered significantly important to find the performance of each classifier. Some of the measures are precision, recall, accuracy, F-score, and AUC [30]. e evaluation based on these measures can be done by the following.…”

Section: Classifiers and Energy Functions Usedmentioning

confidence: 99%

Deep Learning Approach for Stages of Severity Classification in Diabetic Retinopathy Using Color Fundus Retinal Images

Goel

Gupta

Panwar

et al. 2021

Mathematical Problems in Engineering

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Diabetes is a very fast-growing disease in India, with currently more than 72 million patients. Prolonged diabetes (about almost 20 years) can cause serious loss to the tiny blood vessels and neurons in the patient eyes, called diabetic retinopathy (DR). This first causes occlusion and then rapid vision loss. The symptoms of the disease are not very conspicuous in its early stage. The disease is featured by the formation of bloated structures in the retinal area called microaneurysms. Because of negligence, the condition of the eye worsens into the generation of more severe blots and damage to retinal vessels causing complete loss of vision. Early screening and monitoring of DR can reduce the risk of vision loss in patients with high possibilities. But the diabetic retinopathy detection and its classification by a human, is a challenging and error-prone task, because of the complexity of the image captured by color fundus photography. Machine learning algorithms armed with some feature extraction techniques have been employed earlier to detect and classify the levels of DR. However, these techniques provide below-par accuracy. Now, with the advent of deep learning (DL) techniques in computer vision, it has become possible to achieve very high levels of accuracy. DL models are an abstraction of the human brain coupled with the eyes. To create a model from scratch and train it is a cumbersome task requiring a huge amount of images. This deficiency of the DL techniques can be patched up by employing another technique to a task called transfer learning. In this, a DL model is trained on image metadata, and to learn features it used hundreds of classes from the DR fundus images. This enables professionals to create models capable of classifying unseen images into a proper grade or level with acceptable accuracy. This paper proposed a DL model coupled with different classifiers to classify the fundus image into its correct class of severity. We have trained the model on IDRD images and it has proven to show very high accuracy.

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“…Detection of obstacle can be executed by using several sensors that can be categorized as range-based sensor [6] [7] and visionbased sensor [8][9] [10]. Range-based sensor will directly calculate and produce the exact distance of the obstacle from the UAV, for example, ultrasonic sensor (US) [11] , light detection and ranging (LIDAR) [12] [13] , radar [6], Kinect [14], and sonar sensor [15]. The benefits of range-based sensor are affordable and great in estimating the detected obstacle's distance, but lacking in information on the detected obstacle's position or bearing in the surrounding environment.…”

Section: Introductionmentioning

confidence: 99%

Region Detection Technique Using Image Subtraction and Pixel Expansion Cue for Obstacle Detection System on Small – Sized UAV

Wafi Abdul Aziz,

Faiz Ramli

2024

IJCDS

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This research paper is about method of detection of free region and obstacle region by combining image segmentation and frame subtraction method. The application will be further study to be used by Unmanned Aerial Vehicles (UAVs). This method intends to minimize the weight of UAV by avoiding heavy sensors. Objective of this research is to utilize the pixel expansion of object to find free region. K-means segmentation will be used to separate the interest area from the background. Then, segmented image frame will be subtracted and then divided into several grids and the amount of subtracted pixel that has been set into yellow and black color of each grid will be calculated with respect to distance given. The expansion of pixel will be detected as, the distance between image frame coming closer, number of obstacle pixel will be higher. The application of simple LIDAR that emits single ray to frontal obstacle will initiate the camera to capture image frame to be further analyze. Experiment was carried out in close environment with different cases and total of 100 images has been captured that consist of texture obstacle, texture-less obstacle, and multiple obstacles. The findings showed bearable results as the free region detection is 88.0% for texture obstacle and up to 84.0% of free region were successfully detected for texture-less obstacle. Due to lack of cues and texture in texture-less object, the algorithm had difficulties detecting the center of object that expands in static form.

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A Framework for the Generation of Obstacle Data for the Study of Obstacle Detection by Ultrasonic Sensors

Cited by 15 publications

References 27 publications

An IoT Machine Learning-Based Mobile Sensors Unit for Visually Impaired People

An IoT Machine Learning-Based Mobile Sensors Unit for Visually Impaired People

Deep Learning Approach for Stages of Severity Classification in Diabetic Retinopathy Using Color Fundus Retinal Images

Region Detection Technique Using Image Subtraction and Pixel Expansion Cue for Obstacle Detection System on Small – Sized UAV

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