Computer Vision-Based Obstacle Detection Mobile System for Visually Impaired Individuals
Traditional tools, such as canes, are no longer enough to subsist the mobility and orientation of visually impaired people in complex environments. Therefore, technological solutions based on computer vision tasks are presented as promising alternatives to help detect obstacles. Object detection mod...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-05-01
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| Series: | Multimodal Technologies and Interaction |
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| Online Access: | https://www.mdpi.com/2414-4088/9/5/48 |
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| author | Gisel Katerine Bastidas-Guacho Mario Alejandro Paguay Alvarado Patricio Xavier Moreno-Vallejo Patricio Rene Moreno-Costales Nayely Samanta Ocaña Yanza Jhon Carlos Troya Cuestas |
| author_facet | Gisel Katerine Bastidas-Guacho Mario Alejandro Paguay Alvarado Patricio Xavier Moreno-Vallejo Patricio Rene Moreno-Costales Nayely Samanta Ocaña Yanza Jhon Carlos Troya Cuestas |
| author_sort | Gisel Katerine Bastidas-Guacho |
| collection | DOAJ |
| description | Traditional tools, such as canes, are no longer enough to subsist the mobility and orientation of visually impaired people in complex environments. Therefore, technological solutions based on computer vision tasks are presented as promising alternatives to help detect obstacles. Object detection models are easy to couple to mobile systems, do not require a large consumption of resources on mobile phones, and act in real-time to alert users of the presence of obstacles. However, existing object detectors were mostly trained with images from platforms such as Kaggle, and the number of existing objects is still limited. For this reason, this study proposes to implement a mobile system that integrates an object detection model for the identification of obstacles intended for visually impaired people. Additionally, the mobile application integrates multimodal feedback through auditory and haptic interaction, ensuring that users receive real-time obstacle alerts via voice guidance and vibrations, further enhancing accessibility and responsiveness in different navigation contexts. The chosen scenario to develop the obstacle detection application is the Specialized Educational Unit Dr. Luis Benavides for impaired people, which is the source of images for building the dataset for the model and evaluating it with impaired individuals. To determine the best model, the performance of YOLO is evaluated by means of a precision adjustment through the variation of epochs, using a proprietary data set of 7600 diverse images. The YOLO-300 model turned out to be the best, with a mAP of 0.42. |
| format | Article |
| id | doaj-art-c4f22be3a1f04ee180f3ac6c381b1de2 |
| institution | DOAJ |
| issn | 2414-4088 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Multimodal Technologies and Interaction |
| spelling | doaj-art-c4f22be3a1f04ee180f3ac6c381b1de22025-08-20T03:14:29ZengMDPI AGMultimodal Technologies and Interaction2414-40882025-05-01954810.3390/mti9050048Computer Vision-Based Obstacle Detection Mobile System for Visually Impaired IndividualsGisel Katerine Bastidas-Guacho0Mario Alejandro Paguay Alvarado1Patricio Xavier Moreno-Vallejo2Patricio Rene Moreno-Costales3Nayely Samanta Ocaña Yanza4Jhon Carlos Troya Cuestas5Faculty of Computer Science and Electronics, Escuela Superior Politécnica de Chimborazo (ESPOCH), Riobamba 060155, EcuadorFaculty of Computer Science and Electronics, Escuela Superior Politécnica de Chimborazo (ESPOCH), Riobamba 060155, EcuadorFaculty of Business Administration, Escuela Superior Politécnica de Chimborazo (ESPOCH), Riobamba 060155, EcuadorFaculty of Computer Science and Electronics, Escuela Superior Politécnica de Chimborazo (ESPOCH), Riobamba 060155, EcuadorIndependent Researcher, Riobamba 060104, EcuadorIndependent Researcher, Riobamba 060104, EcuadorTraditional tools, such as canes, are no longer enough to subsist the mobility and orientation of visually impaired people in complex environments. Therefore, technological solutions based on computer vision tasks are presented as promising alternatives to help detect obstacles. Object detection models are easy to couple to mobile systems, do not require a large consumption of resources on mobile phones, and act in real-time to alert users of the presence of obstacles. However, existing object detectors were mostly trained with images from platforms such as Kaggle, and the number of existing objects is still limited. For this reason, this study proposes to implement a mobile system that integrates an object detection model for the identification of obstacles intended for visually impaired people. Additionally, the mobile application integrates multimodal feedback through auditory and haptic interaction, ensuring that users receive real-time obstacle alerts via voice guidance and vibrations, further enhancing accessibility and responsiveness in different navigation contexts. The chosen scenario to develop the obstacle detection application is the Specialized Educational Unit Dr. Luis Benavides for impaired people, which is the source of images for building the dataset for the model and evaluating it with impaired individuals. To determine the best model, the performance of YOLO is evaluated by means of a precision adjustment through the variation of epochs, using a proprietary data set of 7600 diverse images. The YOLO-300 model turned out to be the best, with a mAP of 0.42.https://www.mdpi.com/2414-4088/9/5/48CRISP-DMYOLOcomputer visionmobile systemobstacle detection |
| spellingShingle | Gisel Katerine Bastidas-Guacho Mario Alejandro Paguay Alvarado Patricio Xavier Moreno-Vallejo Patricio Rene Moreno-Costales Nayely Samanta Ocaña Yanza Jhon Carlos Troya Cuestas Computer Vision-Based Obstacle Detection Mobile System for Visually Impaired Individuals Multimodal Technologies and Interaction CRISP-DM YOLO computer vision mobile system obstacle detection |
| title | Computer Vision-Based Obstacle Detection Mobile System for Visually Impaired Individuals |
| title_full | Computer Vision-Based Obstacle Detection Mobile System for Visually Impaired Individuals |
| title_fullStr | Computer Vision-Based Obstacle Detection Mobile System for Visually Impaired Individuals |
| title_full_unstemmed | Computer Vision-Based Obstacle Detection Mobile System for Visually Impaired Individuals |
| title_short | Computer Vision-Based Obstacle Detection Mobile System for Visually Impaired Individuals |
| title_sort | computer vision based obstacle detection mobile system for visually impaired individuals |
| topic | CRISP-DM YOLO computer vision mobile system obstacle detection |
| url | https://www.mdpi.com/2414-4088/9/5/48 |
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