Work

Enhancing Smart Outdoor Object Navigation for the Visually Impaired via YOLOv10 With Neighbor Coordinates and C2FCIB Attention Mechanism

Year: 2025

Type: article

Abstract: This research introduces an AI-driven outdoor object detection system aimed at enhancing navigation for visually impaired individuals (VIIs). VIIs often face significant challenges in accessing and interpreting visual information. Recent advancements in computer hardware and deep learning techniques have led to notable progress in developing assistive technologies for VIIs. However, existing datasets often focus on single scenarios and lack sufficient annotations to represent the diverse obstacles encountered in real-world settings. This limitation hinders the development of comprehensive object detection systems tailored to the needs of VIIs. The system utilizes advanced models such as YOLOv8 (Nano, Small, Medium), YOLOv9c, and YOLOv10n, with neighbor coordinates and C2FCIB attention modules trained on the WOTR dataset, which includes 20 classes of common outdoor objects. A comparative study evaluated the performance of these models across key metrics. The YOLOv8m model demonstrated balanced performance with an accuracy of 85.53. YOLOv8n showed slightly lower performance, with an accuracy of 77.05%, and the YOLOv8s model recorded an accuracy of 84.99%, precision and recall of 0.99, 0.89, matching YOLOv8n with an F1 score of 0.74. Similarly, YOLOv9c achieved an accuracy of 79.82%, and the proposed YOLOv10n model with neighbor coordinates and C2FCIB attention modules led with the highest accuracy of 89.33%, precision and recall of 0.99, 0.92, with F1 score of 0.79. A comparative analysis revealed that the proposed YOLOv10n with neighbor coordinates and C2FCIB attention modules achieved the highest accuracy with precision and recall indicating its reliability for assistive applications. In the realm of assistive technologies, similar AI-powered devices have been developed to aid visually impaired individuals. These innovations, alongside the described object detection system, exemplify the potential of AI in creating inclusive solutions that empower visually impaired individuals to navigate their environments more safely and independently.

Source: IEEE Access

DOI: 10.1109/access.2025.3612482

Authors: Abdullah Khan, Dzati Athiar Ramli, Syed Muhammad Zubair Rehman Gillani, Javed Iqbal Bangash, Muhammad Nouman Atta

Institutions: Universiti Sains Malaysia, Sohar University, The University of Agriculture, Peshawar

Language: English

Topic: Advanced Neural Network Applications

Subfield: Computer Vision and Pattern Recognition

Field: Computer Science

Domain: Physical Sciences

SDGs: Good health and well-being, Reduced inequalities

Open Access status: gold

Estimated APC paid: $2,075