ArticleEntropy (Basel, Switzerland)2020
Efficient Multi-Object Detection and Smart Navigation Using Artificial Intelligence for Visually Impaired People.
Article in Entropy (Basel, Switzerland), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers, 2 of them syntheses that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
11 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Digital accessibility in the era of artificial intelligence-Bibliometric analysis and systematic review.Frontiers in artificial intelligence · 2024Pooled it
- A Systematic Review of Urban Navigation Systems for Visually Impaired People.Sensors (Basel, Switzerland) · 2021Pooled it
- Multi-strategy dung beetle optimization for robust indoor object detection and tracking for visually impaired people with hybrid deep learning networks.Scientific reports · 2025Article
- An intelligent object detection and classification framework for assisting visually challenged persons using deep learning and improved crow search optimization.Scientific reports · 2025Article
- Leveraging retinanet based object detection model for assisting visually impaired individuals with metaheuristic optimization algorithm.Scientific reports · 2025Article
- Comprehensive Review: High-Performance Positioning Systems for Navigation and Wayfinding for Visually Impaired People.Sensors (Basel, Switzerland) · 2024Review
- A Technology Aid to Help People with Blindness and Moderate Intellectual Disability Retrieve Common Objects from Storage Units: A Proof-of-Concept Study.Sensors (Basel, Switzerland) · 2024Article
- Enhancing fall risk assessment: instrumenting vision with deep learning during walks.Journal of neuroengineering and rehabilitation · 2024Article
- Assistive Systems for Visually Impaired Persons: Challenges and Opportunities for Navigation Assistance.Sensors (Basel, Switzerland) · 2024Review
- Automatic Fire Detection and Notification System Based on Improved YOLOv4 for the Blind and Visually Impaired.Sensors (Basel, Switzerland) · 2022Article
- Computer Vision Based Automatic Recognition of Pointer Instruments: Data Set Optimization and Reading.Entropy (Basel, Switzerland) · 2021Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
Abstract
Visually impaired people face numerous difficulties in their daily life, and technological interventions may assist them to meet these challenges. This paper proposes an artificial intelligence-based fully automatic assistive technology to recognize different objects, and auditory inputs are provided to the user in real time, which gives better understanding to the visually impaired person about their surroundings. A deep-learning model is trained with multiple images of objects that are highly relevant to the visually impaired person. Training images are augmented and manually annotated to bring more robustness to the trained model. In addition to computer vision-based techniques for object recognition, a distance-measuring sensor is integrated to make the device more comprehensive by recognizing obstacles while navigating from one place to another. The auditory information that is conveyed to the user after scene segmentation and obstacle identification is optimized to obtain more information in less time for faster processing of video frames. The average accuracy of this proposed method is 95.19% and 99.69% for object detection and recognition, respectively. The time complexity is low, allowing a user to perceive the surrounding scene in real time.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.