Intelligent Obstacle Avoidance Method for Blind Travel Based on an Improved YOLO Algorithm and Binocular Vision

With the improvement of deep learning technology, traditional obstacle avoidance approaches for blind people can no longer meet practical needs. In response to the problems of poor adaptability to multiple scenarios and low obstacle avoidance rates in current obstacle avoidance methods for blind people, this study proposes an intelligent obstacle avoidance method for blind people based on an improved You Only Look Once (YOLO) algorithm and binocular vision. This method first improves the YOLOv8 algorithm by introducing an angle loss function, SPD convolution and a more efficient local convolution structure, and proposes an I-YOLOv8-BBOD model suitable for blind people’s travel. Then, based on this model, a blind travel intelligent obstacle avoidance platform is built. The confusion matrix analysis showed that the research model performed the best and had significant advantages in reducing false positives and false negatives. Its precision, recall and F1 score were all above 0.90, indicating the best overall performance. The ablation experiment showed that after the improvement of each module, the mAP and the average accuracy at the 50% IoU threshold increased by 13.19% and 16.00%, significantly improving the detection accuracy. In the example application, the successful obstacle avoidance rate of the proposed obstacle avoidance platform exceeded 90%, and the false alarm rate was below 1.3%. The traditional platform could reach up to 5.4%, which was better than the previous platform. This indicates that the research method can accurately detect obstacles and road conditions, ensuring the safety of visually impaired people’s travel.