Abstract
AbstractThis study introduces an advanced infrared scene detection algorithm, enhancing the YOLOv8 model for aiding visually impaired individuals in navigation. The focus is on the neck network, integrating attention scale sequences to boost multi‐level perception, particularly for small object detection. This is achieved by adding upsampling and downsampling in the P2 module. Additionally, the CIoU loss function is refined with Inner‐SIoU, elevating bounding box detection precision. A distinctive feature of the approach is its monocular distance and velocity measurement integration, which operates independently of external devices, providing direct navigation support for visually impaired people. Further, the enhanced YOLOv8 is adapted for mobile use, employing pruning and lightweight methods, which substantially enhance its practicality. The experimental results on the FLIR and WOTR datasets demonstrate that, compared to the original YOLOv8n, the improved algorithm has achieved a 2.1% and 3.2% increase in , respectively. Furthermore, the has seen a 2.2% and 3.8% improvement. Concurrently, the model size has been reduced by 55% and 60%, and the number of parameters has decreased by 60% and 67%. Compared to other assistive travel methods for visually impaired individuals, our work demonstrates superior practicality.
Published Version
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