Machine vision-based recognition of safety signs in work environments
Bibliographic Data
| ID | 22069364 |
|---|---|
| Authors | Jesús-Ángel Román-Gallego, Jesús‐Ángel Román‐Gallego (0000-0002-2058-6219, Universidad de Salamanca, corresponding author), María-Luisa Pérez-Delgado, María‐Luisa Pérez‐Delgado (0000-0003-1810-0264, Universidad de Salamanca), Miguel Á Conde (0000-0001-5881-7775, Universidad de Salamanca), Marcos Luengo Viñuela (Universidad de Salamanca) |
| Year | 2024 |
| Volume | 12 |
| Pages | 1431757-1431757 |
| Publication date | 2024-11-27 |
| Peer Reviewed | Yes |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Frontiers in Public Health (JOURNAL) |
| Journal identifiers | ISSN: 2296-2565 • E-ISSN: 2296-2565 |
| Publisher | Frontiers Media SA (PUBLISHER • CH) |
| DOI | 10.3389/fpubh.2024.1431757 |
| PMID | 39664547 |
| OpenAlex | W4404774494 |
| Language | EN |
| References cited | 30 |
The field of image recognition is extensively researched, with applications addressing numerous challenges posed by the scientific community. Notably among these challenges are those related to individual safety. This article presents a system designed for the application of image recognition in the realm of Occupational Risk Prevention—a concern of paramount importance due to the imperative of preventing workplace accidents as falls, collisions, or other types of accidents for the benefit of both workers and enterprises. In this study, convolutional neural networks are employed due to their exceptional efficacy in image recognition. Leveraging this technology, the focus is on the recognition of safety signs used in Occupational Risk Prevention. The primary objective is to enable the recognition of these signs regardless of their orientation or potential degradation, phenomena commonly observed due to regular exposure to environmental elements or deliberate defacement. The results of this research substantiate the feasibility of integrating this technology into devices capable of promptly alerting individuals to potential risks. However, to improve classification capabilities, especially for highly degraded or complex images, a larger and more diverse data set might be needed, including real-world images that introduce greater entropy and variability. Implementing such a system would provide workers and companies with a proactive measure against workplace accidents, thereby enhancing overall safety in occupational environments
Computer vision · Human–computer interaction · Computer Science · Engineering · Fire Detection and Safety Systems · Safety Warnings and Signage · Traffic and Road Safety · Artificial Intelligence
| Citation velocity | historical |
|---|---|
| Highly cited | No |