September 20, 2025SensorsOpen Access

TFP-YOLO: Obstacle and Traffic Sign Detection for Assisting Visually Impaired Pedestrians

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The TFP-YOLO algorithm achieves a detection accuracy of 93.9% and precision of 90.2%, enhancing machine guide dog performance.
Incorporating advanced mechanisms like Triplet Attention and Triple Feature Encoding, it improves the understanding of complex environments.
A P2 detection head specifically boosts accuracy for small objects, while WIoU loss function ensures better model generalization.
These enhancements significantly aid in identifying obstacles and traffic signs, providing valuable support for path planning.

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The TFP-YOLO algorithm enhances obstacle and traffic sign detection in visually impaired pedestrians, indicating improved accuracy and robustness.

Zheng et al. (2025) studied this question.

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The TFP-YOLO algorithm enhances obstacle and traffic sign detection in visually impaired pedestrians, indicating improved accuracy and robustness.