Cao, Yu, Liu, MEI, Liu, Jingxing, Wang, Yuan
Accepted: 2024-11-05
The travel and daily activities of visually impaired individuals typically rely on walking sticks, guide dogs, or assistance from others. However, with the advancement of urban planning due to economic development, the complexity of urban layout and road design has significantly increased. Consequently, these traditional methods are no longer sufficient to meet the daily needs of blind individuals. Therefore, it is crucial to prioritize attention towards the visually impaired community and enhance their quality of life. This design focuses on machine vision techniques for denoising, filtering, and target calibration in color information and depth images. Subsequently, machine learning training is employed to achieve image-based functions such as color recognition, obstacle detection, and distance measurement. The analysis results are then converted into audio signals which are outputted through voice feedback to provide users with walking suggestions while alerting them about obstacles that cannot be observed in advance. The ultimate goal is to offer obstacle avoidance services for visually impaired individuals by minimizing the impact of unforeseen obstacles during mobility.