Abstract:

Blind roads and blind road obstacles are important factors that affect the travel safety of blind people. Existing algorithms only deal with blind road segmentation and blind road obstacle detection separately, with low efficiency and high computational complexity. To solve the above problems, this study proposes a multi-task recognition algorithm based on deep learning. The algorithm extracts public features through the backbone network, after the extracted features are fused through the SPP(Spatial Pyramid Pooling)and FPN(Feature Pyramid Networks)networks, they are respectively passed into the segmentation network and the detection network to complete the tasks of blind road segmentation and blind road obstacle detection. In order to make the blind road segmentation smoother, a correction loss function is introduced. In order to improve the recall rate of obstacle detection, the NMS(Non Maximum Suppression) of the detection network is replaced by Soft-NMS. The experimental results show that the algorithm segmentation part MIoU, MPA reach 93.52%, 95.29%, respectively, and the detection part mAP(mean Average Precision)、mAP@0.5 and mAP@0.75 respectively reach 75.58%、91.58%and 74.82%. Compared with using the SegFormer network for blind road segmentation and the RetinaNet network for blind road obstacle detection, this algorithm not only improves the accuracy, but also improves the speed by 73.72%, and the FPS(Frames Per Secon) reaches 18.52. Compared with other comparative algorithms, this algorithm also has a certain improvement in speed and accuracy.

Key words: blind roads segmentation, blind roads obstacle detection, object detection, image segmentation, feature fusion, Transformer, multi-task learning, deep learning