Unstructured Road Vanishing Point Detection Using Pyramid Dual Squeeze Attention-Enhanced HRNet

In unstructured environments, the detection of vanishing points (VP) on the road is a key technology for autonomous driving and robot navigation. In this paper, we propose a VP detection method based on the improved High-Resolution Network (HRNet), which introduces an innovative Pyramid Dual Squeeze Attention module (PDSA) to realize multiscale feature dynamic sensing and adaptive recalibration of channel importance, while effectively suppressing the noise channel interference through the dual pooling strategy. Compared to the fixed size of the the <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$3 \times 3$</tex> convolution in HRNet, our approach provides ricmultiscaleale feature representation and more effective channel space interaction without adding the computational burden. Improves VP detection accuracy in complex scenes while ensuring real-time performance. Results obtained from the Kong dataset experiments show that the mean detection error rate of our approach is only 0.03182, which is significantly better than the HRNet-based approach.